-------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\Andy baker\OneDrive - University of Colorado at Boulder Office 365\My Documents\Research\Paper PID Experiment\Russia\JOP IRT Russia Res
> ults.log
  log type:  text
 opened on:   6 Mar 2018, 20:36:25

. use "`stick'\My Documents\Research\Paper PID Experiment\Russia\andy_baker (all english labels).dta", clear

. svyset [weight=qvec]
(sampling weights assumed)

      pweight: qvec
          VCE: linearized
  Single unit: missing
     Strata 1: <one>
         SU 1: <observations>
        FPC 1: <zero>

. 
. *I. PARTY ID MEASURES
.         *1: Working with the "party closeness" variable 
. gen close=qb1b
(916 missing values generated)

. replace close=0 if qb1a==2 | qb1a==7
(873 real changes made)

. replace close=. if qb1a==8
(0 real changes made)

. replace close=. if qb1b==7 | qb1b==8
(9 real changes made, 9 to missing)

. 
. label define pid 0 Independent 1 "United Russia" 2 "Communist Party of Russia" 3 "Liberal Democratic Party of Russia" 4 "Just Russia" 5 "Other Party" 7 "Ha
> rd to say" 8 "Refusal"

. label values close pid 

. 
. xi, noomit: svy: proportion i.close 
(running proportion on estimation sample)

Survey: Proportion estimation

Number of strata =       1        Number of obs   =      1,550
Number of PSUs   =   1,550        Population size = 1,548.1116
                                  Design df       =      1,549

--------------------------------------------------------------
             |             Linearized
             | Proportion   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
_Iclose_0    |
           0 |   .4381555   .0147943      .4093795    .4673536
           1 |   .5618445   .0147943      .5326464    .5906205
-------------+------------------------------------------------
_Iclose_1    |
           0 |   .7256126   .0126725      .7000691    .7497582
           1 |   .2743874   .0126725      .2502418    .2999309
-------------+------------------------------------------------
_Iclose_2    |
           0 |     .90851   .0100002      .8869126    .9263253
           1 |     .09149   .0100002      .0736747    .1130874
-------------+------------------------------------------------
_Iclose_3    |
           0 |    .951169   .0070045      .9354457    .9632132
           1 |    .048831   .0070045      .0367868    .0645543
-------------+------------------------------------------------
_Iclose_4    |
           0 |   .9839986   .0037036      .9748533    .9898525
           1 |   .0160014   .0037036      .0101475    .0251467
-------------+------------------------------------------------
_Iclose_5    |
           0 |   .9925545   .0023965      .9860274    .9960448
           1 |   .0074455   .0023965      .0039552    .0139726
--------------------------------------------------------------

. 
. recode close (0=1) (.=.) (else=0), gen(close_indep)
(1550 differences between close and close_indep)

. recode close (1=1) (.=.) (else=0), gen(close_ur)
(193 differences between close and close_ur)

. recode close (2=1) (.=.) (else=0), gen(close_comm)
(677 differences between close and close_comm)

. recode close (3=1) (.=.) (else=0), gen(close_LDPR)
(677 differences between close and close_LDPR)

. recode close (4=1) (.=.) (else=0), gen(close_JR)
(677 differences between close and close_JR)

. recode close (5=1) (.=.) (else=0), gen(close_other)
(677 differences between close and close_other)

. 
.         *2: Working with the "preference--explicit" variable, treatment A
. recode qb2a (.=.) (6=0) (7=0) (8=.), gen(prefexp)
(149 differences between qb2a and prefexp)

. label values prefexp pid

. 
. xi, noomit: svy: proportion i.prefexp
(running proportion on estimation sample)

Survey: Proportion estimation

Number of strata =       1        Number of obs   =        433
Number of PSUs   =     433        Population size = 422.400199
                                  Design df       =        432

--------------------------------------------------------------
             |             Linearized
             | Proportion   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
_Iprefexp_0  |
           0 |   .6897227   .0263739      .6356653     .739052
           1 |   .3102773   .0263739       .260948    .3643347
-------------+------------------------------------------------
_Iprefexp_1  |
           0 |   .5184892   .0282515       .462962    .5735635
           1 |   .4815108   .0282515      .4264365     .537038
-------------+------------------------------------------------
_Iprefexp_2  |
           0 |   .8909916   .0194067      .8466011    .9236941
           1 |   .1090084   .0194067      .0763059    .1533989
-------------+------------------------------------------------
_Iprefexp_3  |
           0 |   .9340778   .0147206      .8985497    .9577489
           1 |   .0659222   .0147206      .0422511    .1014503
-------------+------------------------------------------------
_Iprefexp_4  |
           0 |   .9754618   .0077639      .9545733    .9868772
           1 |   .0245382   .0077639      .0131228    .0454267
-------------+------------------------------------------------
_Iprefexp_5  |
           0 |   .9912569   .0054057      .9708234    .9974181
           1 |   .0087431   .0054057      .0025819    .0291766
--------------------------------------------------------------

. 
. recode prefexp (0=1) (.=.) (else=0), gen(prefexp_indep)
(433 differences between prefexp and prefexp_indep)

. recode prefexp (1=1) (.=.) (else=0), gen(prefexp_ur)
(78 differences between prefexp and prefexp_ur)

. recode prefexp (2=1) (.=.) (else=0), gen(prefexp_comm)
(306 differences between prefexp and prefexp_comm)

. recode prefexp (3=1) (.=.) (else=0), gen(prefexp_LDPR)
(306 differences between prefexp and prefexp_LDPR)

. recode prefexp (4=1) (.=.) (else=0), gen(prefexp_JR)
(306 differences between prefexp and prefexp_JR)

. recode prefexp (5=1) (.=.) (else=0), gen(prefexp_other)
(306 differences between prefexp and prefexp_other)

. 
.         *3: Working with the "identity" variable, treatment B
. recode qb2b (.=.) (6=0) (7=0) (8=.), gen(identity)
(186 differences between qb2b and identity)

. label values identity pid

. 
. xi, noomit: svy: proportion i.identity
(running proportion on estimation sample)

Survey: Proportion estimation

Number of strata =       1        Number of obs   =        411
Number of PSUs   =     411        Population size =  417.51249
                                  Design df       =        410

--------------------------------------------------------------
             |             Linearized
             | Proportion   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
_Iidentity_0 |
           0 |   .6127999   .0278103      .5569261    .6658552
           1 |   .3872001   .0278103      .3341448    .4430739
-------------+------------------------------------------------
_Iidentity_1 |
           0 |   .6003749   .0276909      .5449159    .6533746
           1 |   .3996251   .0276909      .3466254    .4550841
-------------+------------------------------------------------
_Iidentity_2 |
           0 |   .9015811   .0188808      .8577389    .9329679
           1 |   .0984189   .0188808      .0670321    .1422611
-------------+------------------------------------------------
_Iidentity_3 |
           0 |   .9331501   .0159787      .8940306    .9584987
           1 |   .0668499   .0159787      .0415013    .1059694
-------------+------------------------------------------------
_Iidentity_4 |
           0 |   .9611961   .0124061      .9279625    .9794375
           1 |   .0388039   .0124061      .0205625    .0720375
-------------+------------------------------------------------
_Iidentity_5 |
           0 |   .9908979   .0047546      .9747618    .9967517
           1 |   .0091021   .0047546      .0032483    .0252382
--------------------------------------------------------------

. 
. recode identity (0=1) (.=.) (else=0), gen(identity_indep)
(411 differences between identity and identity_indep)

. recode identity (1=1) (.=.) (else=0), gen(identity_ur)
(63 differences between identity and identity_ur)

. recode identity (2=1) (.=.) (else=0), gen(identity_comm)
(245 differences between identity and identity_comm)

. recode identity (3=1) (.=.) (else=0), gen(identity_LDPR)
(245 differences between identity and identity_LDPR)

. recode identity (4=1) (.=.) (else=0), gen(identity_JR)
(245 differences between identity and identity_JR)

. recode identity (5=1) (.=.) (else=0), gen(identity_other)
(245 differences between identity and identity_other)

. 
.         *4: Working with the "preference--implicit" variable, treatment C
. recode qb2c (.=.) (6=0) (7=0) (8=.), gen(prefimp)
(268 differences between qb2c and prefimp)

. label values prefimp pid

. 
. xi, noomit: svy: proportion i.prefimp
(running proportion on estimation sample)

Survey: Proportion estimation

Number of strata =       1        Number of obs   =        684
Number of PSUs   =     684        Population size =  694.63414
                                  Design df       =        683

--------------------------------------------------------------
             |             Linearized
             | Proportion   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
_Iprefimp_0  |
           0 |   .6609124   .0209397      .6186698     .700739
           1 |   .3390876   .0209397       .299261    .3813302
-------------+------------------------------------------------
_Iprefimp_1  |
           0 |   .5898981   .0216237      .5468728    .6315903
           1 |   .4101019   .0216237      .3684097    .4531272
-------------+------------------------------------------------
_Iprefimp_2  |
           0 |   .8719878   .0178883      .8325768    .9032002
           1 |   .1280122   .0178883      .0967998    .1674232
-------------+------------------------------------------------
_Iprefimp_3  |
           0 |   .9209359   .0129833      .8913911     .942958
           1 |   .0790641   .0129833       .057042    .1086089
-------------+------------------------------------------------
_Iprefimp_4  |
           0 |   .9705851   .0078604      .9505375    .9826555
           1 |   .0294149   .0078604      .0173445    .0494625
-------------+------------------------------------------------
_Iprefimp_5  |
           0 |   .9856806   .0055921      .9693443    .9933709
           1 |   .0143194   .0055921      .0066291    .0306557
--------------------------------------------------------------

. 
. recode prefimp (0=1) (.=.) (else=0), gen(prefimp_indep)
(684 differences between prefimp and prefimp_indep)

. recode prefimp (1=1) (.=.) (else=0), gen(prefimp_ur)
(131 differences between prefimp and prefimp_ur)

. recode prefimp (2=1) (.=.) (else=0), gen(prefimp_comm)
(448 differences between prefimp and prefimp_comm)

. recode prefimp (3=1) (.=.) (else=0), gen(prefimp_LDPR)
(448 differences between prefimp and prefimp_LDPR)

. recode prefimp (4=1) (.=.) (else=0), gen(prefimp_JR)
(448 differences between prefimp and prefimp_JR)

. recode prefimp (5=1) (.=.) (else=0), gen(prefimp_other)
(448 differences between prefimp and prefimp_other)

. 
. *II. OTHER MEASURES RELATED TO PARTY ID
.         *1: Party nonrejection
. recode qb3b_1 (1=0) (else=1), gen(nonreject_ur)
(1602 differences between qb3b_1 and nonreject_ur)

. replace nonreject_ur=. if qb3a==8
(138 real changes made, 138 to missing)

. 
. recode qb3b_2 (1=0) (else=1), gen(nonreject_comm)
(1602 differences between qb3b_2 and nonreject_comm)

. replace nonreject_comm=. if qb3a==8
(138 real changes made, 138 to missing)

. 
. recode qb3b_3 (1=0) (else=1), gen(nonreject_LDPR)
(1602 differences between qb3b_3 and nonreject_LDPR)

. replace nonreject_LDPR=. if qb3a==8
(138 real changes made, 138 to missing)

. 
. recode qb3b_4 (1=0) (else=1), gen(nonreject_JR)
(1602 differences between qb3b_4 and nonreject_JR)

. replace nonreject_JR=. if qb3a==8
(138 real changes made, 138 to missing)

. 
.         *2: Feeling thermometers
. recode qb4a (0/5=0) (6/15=1) (16/25=2) (26/35=3) (36/45=4) (46/55=5) (56/65=6) (66/75=7) (76/85=8) (86/95=9) (96/100=10) (else=.) , gen(ft_ur)
(1479 differences between qb4a and ft_ur)

. recode qb4b (0/5=0) (6/15=1) (16/25=2) (26/35=3) (36/45=4) (46/55=5) (56/65=6) (66/75=7) (76/85=8) (86/95=9) (96/100=10) (else=.) , gen(ft_comm)
(1375 differences between qb4b and ft_comm)

. recode qb4c (0/5=0) (6/15=1) (16/25=2) (26/35=3) (36/45=4) (46/55=5) (56/65=6) (66/75=7) (76/85=8) (86/95=9) (96/100=10) (else=.) , gen(ft_LDPR)
(1331 differences between qb4c and ft_LDPR)

. 
.         *3: Duma vote
. label define dumavote 1 "Green Alliance - People's Party (Oleg Mitvol)" 2 "Civic Platform (R.Shayhutdinov)" 3 "United Russia (Medvedev)" 4 "The Communist P
> arty (KPRF) (Zyuganov)"  5 "Liberal Democratic Party of Russia (LDPR) (Vladimir Zhirinovsky)" 6 "Progress Party (A.Navalny)" 7 "Patriots of Russia (G.Semig
> in)" 8 "The Republican Party of Russia - Parnassus (Kasyanov)" 9 "Homeland (Rogozin)" 10 "Just Russia (Mironov)" 11 "Apple (Mitrokhin)" 12 "Others" 13 "Wou
> ld not vote" 14 "Hard to say"

. label values q27b dumavote

. 
. recode q27b (3=1) (.=.) (else=0), gen(dumavote_ur)
(1602 differences between q27b and dumavote_ur)

. recode q27b (4=1) (.=.) (else=0), gen(dumavote_comm)
(1602 differences between q27b and dumavote_comm)

. recode q27b (5=1) (.=.) (else=0), gen(dumavote_LDPR)
(1602 differences between q27b and dumavote_LDPR)

. 
.         *4: Pres vote (although not used because too retrospective)
. label define presvote 1 "The Communist Party (KPRF) (Zyuganov)" 2 "Liberal Democratic Party of Russia (LDPR) (Vladimir Zhirinovsky)" 3 "Just Russia (Mirono
> v)" 4 "Mikhail Prokhorov" 5 "United Russia (Vladimir Putin)" 8 "Spoiled" 9 "Don't remember"  

. label values qd1b presvote

. 
. recode qd1b (5=1) (.=.) (else=0), gen(presvote_ur)
(1117 differences between qd1b and presvote_ur)

. recode qd1b (1=1) (.=.) (else=0), gen(presvote_comm)
(1050 differences between qd1b and presvote_comm)

. recode qd1b (2=1) (.=.) (else=0), gen(presvote_LDPR)
(1117 differences between qd1b and presvote_LDPR)

. 
. replace presvote_ur =. if qd1a==9
(0 real changes made)

. replace presvote_comm =. if qd1a==9
(0 real changes made)

. replace presvote_LDPR =. if qd1a==9
(0 real changes made)

. 
. *III. Descriptives
. egen pref_comm=rsum(prefexp_comm prefimp_comm)

. replace pref_comm=. if prefexp_comm==. & prefimp_comm==.
(485 real changes made, 485 to missing)

. 
. summ prefexp_ur prefimp_ur identity_ur close_ur nonreject_ur dumavote_ur ft_ur prefexp_LDPR prefimp_LDPR identity_LDPR close_LDPR nonreject_LDPR dumavote_L
> DPR ft_LDPR 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  prefexp_ur |        433    .5265589    .4998717          0          1
  prefimp_ur |        684    .4634503    .4990272          0          1
 identity_ur |        411    .4428224    .4973253          0          1
    close_ur |      1,550    .3122581     .463564          0          1
nonreject_ur |      1,464    .8907104    .3121089          0          1
-------------+---------------------------------------------------------
 dumavote_ur |      1,602    .4506866    .4977176          0          1
       ft_ur |      1,313    6.169078    3.352791          0         10
prefexp_LDPR |        433    .0577367    .2335147          0          1
prefimp_LDPR |        684    .0657895    .2480952          0          1
identity_L~R |        411    .0462287     .210236          0          1
-------------+---------------------------------------------------------
  close_LDPR |      1,550    .0367742    .1882677          0          1
nonreject~PR |      1,464    .8647541    .3421029          0          1
dumavote_L~R |      1,602    .0493134    .2165891          0          1
     ft_LDPR |      1,237    3.189167    2.722276          0         10

. summ pref_comm identity_comm close_comm dumavote_comm nonreject_comm ft_comm

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   pref_comm |      1,117    .0895255    .2856287          0          1
identity_c~m |        411    .0681265      .25227          0          1
  close_comm |      1,550    .0670968    .2502703          0          1
dumavote_c~m |      1,602    .0786517    .2692784          0          1
nonreject_~m |      1,464    .9057377    .2922931          0          1
-------------+---------------------------------------------------------
     ft_comm |      1,235     3.77085    2.953194          0         10

. svy: mean prefexp_ur 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        433
Number of PSUs   =     433        Population size = 422.400199
                                  Design df       =        432

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  prefexp_ur |   .4815108   .0282515      .4259832    .5370383
--------------------------------------------------------------

. estat sd

-------------------------------------
             |       Mean   Std. Dev.
-------------+-----------------------
  prefexp_ur |   .4815108     .500236
-------------------------------------

. 
. svy: mean prefimp_ur 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        684
Number of PSUs   =     684        Population size =  694.63414
                                  Design df       =        683

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  prefimp_ur |   .4101019   .0216237       .367645    .4525587
--------------------------------------------------------------

. estat sd

-------------------------------------
             |       Mean   Std. Dev.
-------------+-----------------------
  prefimp_ur |   .4101019    .4922119
-------------------------------------

. 
. svy: mean identity_ur 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        411
Number of PSUs   =     411        Population size =  417.51249
                                  Design df       =        410

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
 identity_ur |   .3996251   .0276909      .3451913    .4540588
--------------------------------------------------------------

. estat sd

-------------------------------------
             |       Mean   Std. Dev.
-------------+-----------------------
 identity_ur |   .3996251    .4904182
-------------------------------------

. 
. svy: mean close_ur 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =      1,550
Number of PSUs   =   1,550        Population size = 1,548.1116
                                  Design df       =      1,549

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    close_ur |   .2743874   .0126725      .2495303    .2992446
--------------------------------------------------------------

. estat sd

-------------------------------------
             |       Mean   Std. Dev.
-------------+-----------------------
    close_ur |   .2743874    .4463491
-------------------------------------

. 
. svy: mean nonreject_ur 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =      1,464
Number of PSUs   =   1,464        Population size = 1,472.0154
                                  Design df       =      1,463

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
nonreject_ur |   .8818248   .0101188      .8619759    .9016737
--------------------------------------------------------------

. estat sd

-------------------------------------
             |       Mean   Std. Dev.
-------------+-----------------------
nonreject_ur |   .8818248    .3229258
-------------------------------------

. 
. svy: mean dumavote_ur 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =      1,602
Number of PSUs   =   1,602        Population size =      1,602
                                  Design df       =      1,601

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
 dumavote_ur |   .3992901    .014046      .3717396    .4268405
--------------------------------------------------------------

. estat sd

-------------------------------------
             |       Mean   Std. Dev.
-------------+-----------------------
 dumavote_ur |   .3992901    .4899054
-------------------------------------

. 
. svy: mean ft_ur 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =      1,313
Number of PSUs   =   1,313        Population size = 1,303.6494
                                  Design df       =      1,312

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       ft_ur |   5.806643   .1134764      5.584028    6.029258
--------------------------------------------------------------

. estat sd

-------------------------------------
             |       Mean   Std. Dev.
-------------+-----------------------
       ft_ur |   5.806643    3.437276
-------------------------------------

. 
. svy: mean prefexp_LDPR 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        433
Number of PSUs   =     433        Population size = 422.400199
                                  Design df       =        432

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
prefexp_LDPR |   .0659222   .0147206      .0369892    .0948552
--------------------------------------------------------------

. estat sd

-------------------------------------
             |       Mean   Std. Dev.
-------------+-----------------------
prefexp_LDPR |   .0659222    .2484331
-------------------------------------

. 
. svy: mean prefimp_LDPR 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        684
Number of PSUs   =     684        Population size =  694.63414
                                  Design df       =        683

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
prefimp_LDPR |   .0790641   .0129833      .0535721    .1045561
--------------------------------------------------------------

. estat sd

-------------------------------------
             |       Mean   Std. Dev.
-------------+-----------------------
prefimp_LDPR |   .0790641    .2700362
-------------------------------------

. 
. svy: mean identity_LDPR 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        411
Number of PSUs   =     411        Population size =  417.51249
                                  Design df       =        410

---------------------------------------------------------------
              |             Linearized
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
identity_LDPR |   .0668499   .0159787      .0354394    .0982604
---------------------------------------------------------------

. estat sd

-------------------------------------
             |       Mean   Std. Dev.
-------------+-----------------------
identity_L~R |   .0668499    .2500663
-------------------------------------

. 
. svy: mean close_LDPR 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =      1,550
Number of PSUs   =   1,550        Population size = 1,548.1116
                                  Design df       =      1,549

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  close_LDPR |    .048831   .0070045      .0350917    .0625703
--------------------------------------------------------------

. estat sd

-------------------------------------
             |       Mean   Std. Dev.
-------------+-----------------------
  close_LDPR |    .048831    .2155842
-------------------------------------

. 
. svy: mean nonreject_LDPR 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =      1,464
Number of PSUs   =   1,464        Population size = 1,472.0154
                                  Design df       =      1,463

----------------------------------------------------------------
               |             Linearized
               |       Mean   Std. Err.     [95% Conf. Interval]
---------------+------------------------------------------------
nonreject_LDPR |   .8680062   .0103423      .8477189    .8882935
----------------------------------------------------------------

. estat sd

-------------------------------------
             |       Mean   Std. Dev.
-------------+-----------------------
nonreject~PR |   .8680062    .3385997
-------------------------------------

. 
. svy: mean dumavote_LDPR 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =      1,602
Number of PSUs   =   1,602        Population size =      1,602
                                  Design df       =      1,601

---------------------------------------------------------------
              |             Linearized
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dumavote_LDPR |   .0599891   .0074845      .0453087    .0746694
---------------------------------------------------------------

. estat sd

-------------------------------------
             |       Mean   Std. Dev.
-------------+-----------------------
dumavote_L~R |   .0599891    .2375408
-------------------------------------

. 
. svy: mean ft_LDPR 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =      1,237
Number of PSUs   =   1,237        Population size = 1,233.2186
                                  Design df       =      1,236

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
     ft_LDPR |   3.310602   .0992942      3.115799    3.505406
--------------------------------------------------------------

. estat sd

-------------------------------------
             |       Mean   Std. Dev.
-------------+-----------------------
     ft_LDPR |   3.310602    2.841544
-------------------------------------

.  
. *IV. Randomization Check
. gen exp=1 if qb2a~=.
(1,147 missing values generated)

. replace exp=2 if qb2b~=.
(431 real changes made)

. replace exp=3 if qb2c~=.
(716 real changes made)

. recode qs1 2=1 1=0
(qs1: 1602 changes made)

. bysort exp: ci mean close_indep close_ur qs1 qs2 qrnp

-------------------------------------------------------------------------------------------------------------------------------------------------------------
-> exp = 1

    Variable |        Obs        Mean    Std. Err.       [95% Conf. Interval]
-------------+---------------------------------------------------------------
 close_indep |        440         .55    .0237441        .5033338    .5966662
    close_ur |        440    .3386364    .0225868        .2942447    .3830281
         qs1 |        455    .5340659    .0234116        .4880573    .5800745
         qs2 |        455    44.92527    .7640424        43.42378    46.42677
        qrnp |        455     3.38022    .0591701        3.263939    3.496501

-------------------------------------------------------------------------------------------------------------------------------------------------------------
-> exp = 2

    Variable |        Obs        Mean    Std. Err.       [95% Conf. Interval]
-------------+---------------------------------------------------------------
 close_indep |        421    .5748219    .0241228        .5274054    .6222383
    close_ur |        421    .3182898    .0227293        .2736124    .3629672
         qs1 |        431    .5522042    .0239804        .5050709    .5993375
         qs2 |        431    43.79118    .7851039        42.24806     45.3343
        qrnp |        431    3.306265    .0635966        3.181266    3.431263

-------------------------------------------------------------------------------------------------------------------------------------------------------------
-> exp = 3

    Variable |        Obs        Mean    Std. Err.       [95% Conf. Interval]
-------------+---------------------------------------------------------------
 close_indep |        689    .5645864    .0189026        .5274726    .6017001
    close_ur |        689    .2917271    .0173298        .2577014    .3257529
         qs1 |        716    .5530726    .0185933        .5165686    .5895766
         qs2 |        716    44.12709    .6164323        42.91686    45.33733
        qrnp |        716    3.322626    .0494623        3.225517    3.419735

. 
. *V: Factor Analysis check for unidimensionality
. polychoricpca prefexp_ur close_ur dumavote_ur ft_ur 

Polychoric correlation matrix

              prefexp_ur     close_ur  dumavote_ur        ft_ur
 prefexp_ur            1
   close_ur    .80528154            1
dumavote_ur    .93439561    .87389402            1
      ft_ur    .80808406    .67990087    .72944269            1

Principal component analysis

 k  |  Eigenvalues  |  Proportion explained  |  Cum. explained
----+---------------+------------------------+------------------
  1 |    3.421399   |    0.855350            |   0.855350
  2 |    0.353170   |    0.088292            |   0.943642
  3 |    0.180243   |    0.045061            |   0.988703
  4 |    0.045188   |    0.011297            |   1.000000

. polychoricpca prefimp_ur close_ur dumavote_ur ft_ur 

Polychoric correlation matrix

              prefimp_ur     close_ur  dumavote_ur        ft_ur
 prefimp_ur            1
   close_ur    .89074762            1
dumavote_ur    .93065706    .87264408            1
      ft_ur    .79845543    .72381405    .78585634            1

Principal component analysis

 k  |  Eigenvalues  |  Proportion explained  |  Cum. explained
----+---------------+------------------------+------------------
  1 |    3.505630   |    0.876408            |   0.876408
  2 |    0.299987   |    0.074997            |   0.951404
  3 |    0.127296   |    0.031824            |   0.983228
  4 |    0.067087   |    0.016772            |   1.000000

. polychoricpca identity_ur close_ur dumavote_ur ft_ur

Polychoric correlation matrix

             identity_ur     close_ur  dumavote_ur        ft_ur
identity_ur            1
   close_ur    .90111479            1
dumavote_ur    .92116589    .86978191            1
      ft_ur    .81667001    .81148597    .80418702            1

Principal component analysis

 k  |  Eigenvalues  |  Proportion explained  |  Cum. explained
----+---------------+------------------------+------------------
  1 |    3.564018   |    0.891004            |   0.891004
  2 |    0.231627   |    0.057907            |   0.948911
  3 |    0.131730   |    0.032933            |   0.981844
  4 |    0.072625   |    0.018156            |   1.000000

. 
. polychoricpca prefexp_LDPR close_LDPR dumavote_LDPR ft_LDPR 

Polychoric correlation matrix

                prefexp_LDPR     close_LDPR  dumavote_LDPR        ft_LDPR
 prefexp_LDPR              1
   close_LDPR      .95990124              1
dumavote_LDPR      .97488447      .93180029              1
      ft_LDPR      .79622057      .75015274      .66889609              1

Principal component analysis

 k  |  Eigenvalues  |  Proportion explained  |  Cum. explained
----+---------------+------------------------+------------------
  1 |    3.552773   |    0.888193            |   0.888193
  2 |    0.376746   |    0.094186            |   0.982380
  3 |    0.064789   |    0.016197            |   0.998577
  4 |    0.005693   |    0.001423            |   1.000000

. polychoricpca prefimp_LDPR close_LDPR dumavote_LDPR ft_LDPR 

Polychoric correlation matrix

                prefimp_LDPR     close_LDPR  dumavote_LDPR        ft_LDPR
 prefimp_LDPR              1
   close_LDPR      .96696972              1
dumavote_LDPR      .99189606      .97828934              1
      ft_LDPR      .63671831      .71290968      .69914015              1

Principal component analysis

 k  |  Eigenvalues  |  Proportion explained  |  Cum. explained
----+---------------+------------------------+------------------
  1 |    3.514696   |    0.878674            |   0.878674
  2 |    0.450239   |    0.112560            |   0.991234
  3 |    0.030801   |    0.007700            |   0.998934
  4 |    0.004263   |    0.001066            |   1.000000

. polychoricpca identity_LDPR close_LDPR dumavote_LDPR ft_LDPR

Polychoric correlation matrix

               identity_LDPR     close_LDPR  dumavote_LDPR        ft_LDPR
identity_LDPR              1
   close_LDPR      .96410438              1
dumavote_LDPR      .92267025      .92158038              1
      ft_LDPR      .78112913      .71085222      .62785608              1

Principal component analysis

 k  |  Eigenvalues  |  Proportion explained  |  Cum. explained
----+---------------+------------------------+------------------
  1 |    3.478196   |    0.869549            |   0.869549
  2 |    0.418752   |    0.104688            |   0.974237
  3 |    0.074999   |    0.018750            |   0.992987
  4 |    0.028053   |    0.007013            |   1.000000

. 
. polychoricpca pref_comm close_comm dumavote_comm ft_comm 

Polychoric correlation matrix

                   pref_comm     close_comm  dumavote_comm        ft_comm
    pref_comm              1
   close_comm      .96017005              1
dumavote_comm      .97572422      .95344198              1
      ft_comm      .80009382      .76504133      .79644151              1

Principal component analysis

 k  |  Eigenvalues  |  Proportion explained  |  Cum. explained
----+---------------+------------------------+------------------
  1 |    3.632543   |    0.908136            |   0.908136
  2 |    0.295427   |    0.073857            |   0.981992
  3 |    0.048415   |    0.012104            |   0.994096
  4 |    0.023616   |    0.005904            |   1.000000

. polychoricpca identity_comm close_comm dumavote_comm ft_comm

Polychoric correlation matrix

               identity_comm     close_comm  dumavote_comm        ft_comm
identity_comm              1
   close_comm       .9438926              1
dumavote_comm      .97545264      .95235183              1
      ft_comm       .8202684      .71607533      .78164677              1

Principal component analysis

 k  |  Eigenvalues  |  Proportion explained  |  Cum. explained
----+---------------+------------------------+------------------
  1 |    3.603177   |    0.900794            |   0.900794
  2 |    0.325937   |    0.081484            |   0.982278
  3 |    0.048675   |    0.012169            |   0.994447
  4 |    0.022211   |    0.005553            |   1.000000

. 
. *VI. Criterion-Validity check
. egen preference=rmax(prefimp prefexp)
(485 missing values generated)

. label values preference pid

. 
. gen pref_close_ur=.
(1,602 missing values generated)

. replace pref_close_ur=2 if close_ur==1 
(484 real changes made)

. replace pref_close_ur=1 if preference==1 & close_indep==1
(198 real changes made)

. replace pref_close_ur=0 if preference==0 & close==0
(319 real changes made)

. replace pref_close_ur=. if pref_close==2 & preference~=1 & preference~=0
(145 real changes made, 145 to missing)

. 
. gen pref_close_LDPR=.
(1,602 missing values generated)

. replace pref_close_LDPR=2 if close_LDPR==1 
(57 real changes made)

. replace pref_close_LDPR=1 if preference==3 & close_indep==1
(23 real changes made)

. replace pref_close_LDPR=0 if preference==0 & close==0
(319 real changes made)

. replace pref_close_LDPR=. if pref_close_LDPR==2 & preference~=3 & preference~=0
(14 real changes made, 14 to missing)

. 
. gen pref_close_comm=.
(1,602 missing values generated)

. replace pref_close_comm=2 if close_comm==1 
(104 real changes made)

. replace pref_close_comm=1 if preference==2 & close_indep==1
(33 real changes made)

. replace pref_close_comm=0 if preference==0 & close==0
(319 real changes made)

. replace pref_close_comm=. if pref_close_comm==2 & preference~=2 & preference~=0
(35 real changes made, 35 to missing)

. 
. recode q27b 3=1 13/14=. .=. else=0, gen(dumavoteUR)
(1602 differences between q27b and dumavoteUR)

. recode q27b 5=1 13/14=. .=. else=0, gen(dumavoteLDPR)
(1602 differences between q27b and dumavoteLDPR)

. recode q27b 4=1 13/14=. .=. else=0, gen(dumavotecomm)
(1602 differences between q27b and dumavotecomm)

. recode q40aa 9=. 1=4 2=3 3=2 4=1
(q40aa: 1602 changes made)

. recode q40ab 9=. 1=4 2=3 3=2 4=1
(q40ab: 1602 changes made)

. 
. local i=0

. while `i'<=2 {
  2. svy: mean dumavoteUR if pref_close_ur==`i'
  3. local i=`i'+1
  4. }
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =         51
Number of PSUs   =      51        Population size =   46.11605
                                  Design df       =         50

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  dumavoteUR |   .6110061    .078536      .4532619    .7687504
--------------------------------------------------------------
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        148
Number of PSUs   =     148        Population size =  138.96675
                                  Design df       =        147

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  dumavoteUR |   .9673781    .013667      .9403689    .9943873
--------------------------------------------------------------
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        323
Number of PSUs   =     323        Population size =  282.82175
                                  Design df       =        322

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  dumavoteUR |   .9663079   .0142294      .9383137    .9943022
--------------------------------------------------------------

. 
. *Putin approval
. local i=0

. while `i'<=2 {
  2. svy: mean q40aa if pref_close_ur==`i'
  3. local i=`i'+1
  4. }
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        307
Number of PSUs   =     307        Population size =  302.41986
                                  Design df       =        306

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       q40aa |   2.953231   .0620308       2.83117    3.075292
--------------------------------------------------------------
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        190
Number of PSUs   =     190        Population size =  176.81421
                                  Design df       =        189

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       q40aa |   3.417528   .0602962      3.298588    3.536468
--------------------------------------------------------------
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        336
Number of PSUs   =     336        Population size =  295.35676
                                  Design df       =        335

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       q40aa |   3.544966   .0365383      3.473093     3.61684
--------------------------------------------------------------

. 
. *Gov't approval
. local i=0

. while `i'<=2 {
  2. svy: mean q40ab if pref_close_ur==`i'
  3. local i=`i'+1
  4. }
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        294
Number of PSUs   =     294        Population size =  284.59479
                                  Design df       =        293

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       q40ab |   2.269289   .0656833      2.140018    2.398559
--------------------------------------------------------------
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        182
Number of PSUs   =     182        Population size = 169.694519
                                  Design df       =        181

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       q40ab |   2.970305   .0716918      2.828846    3.111764
--------------------------------------------------------------
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        320
Number of PSUs   =     320        Population size =  282.10444
                                  Design df       =        319

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       q40ab |   3.092253   .0479931       2.99783    3.186676
--------------------------------------------------------------

. 
. recode q5b 2=0
(q5b: 533 changes made)

. *Medvedev approval
. local i=0

. while `i'<=2 {
  2. svy: mean q5b if pref_close_ur==`i'
  3. local i=`i'+1
  4. }
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        313
Number of PSUs   =     313        Population size =  310.28564
                                  Design df       =        312

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         q5b |   .4797274   .0323849      .4160071    .5434478
--------------------------------------------------------------
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        195
Number of PSUs   =     195        Population size =   181.9725
                                  Design df       =        194

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         q5b |   .7533567    .036971        .68044    .8262733
--------------------------------------------------------------
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        334
Number of PSUs   =     334        Population size =  293.91428
                                  Design df       =        333

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         q5b |   .8785849   .0200629       .839119    .9180509
--------------------------------------------------------------

. 
. local i=0

. while `i'<=2 {
  2. svy: mean dumavoteLDPR if pref_close_LDPR==`i'
  3. local i=`i'+1
  4. }
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =         51
Number of PSUs   =      51        Population size =   46.11605
                                  Design df       =         50

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
dumavoteLDPR |   .0136772   .0137892     -.0140193    .0413737
--------------------------------------------------------------
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =         14
Number of PSUs   =      14        Population size =   14.15699
                                  Design df       =         13

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
dumavoteLDPR |   .9068241   .0922805      .7074641    1.106184
--------------------------------------------------------------
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =         40
Number of PSUs   =      40        Population size = 51.3183995
                                  Design df       =         39

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
dumavoteLDPR |   .9815522   .0186354      .9438587    1.019246
--------------------------------------------------------------

. 
. local i=0

. while `i'<=2 {
  2. svy: mean dumavotecomm if pref_close_comm==`i'
  3. local i=`i'+1
  4. }
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =         51
Number of PSUs   =      51        Population size =   46.11605
                                  Design df       =         50

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
dumavotecomm |   .0134569   .0135701     -.0137995    .0407133
--------------------------------------------------------------
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =         24
Number of PSUs   =      24        Population size = 30.7041201
                                  Design df       =         23

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
dumavotecomm |   .8781453   .0754007      .7221672    1.034124
--------------------------------------------------------------
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =         61
Number of PSUs   =      61        Population size = 87.5854198
                                  Design df       =         60

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
dumavotecomm |   .9699959   .0223934      .9252024    1.014789
--------------------------------------------------------------

. 
. 
. svy: mean dumavoteUR if pref_close_ur~=.
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        522
Number of PSUs   =     522        Population size = 467.904549
                                  Design df       =        521

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  dumavoteUR |   .9316077   .0136175      .9048558    .9583596
--------------------------------------------------------------

. estat sd

-------------------------------------
             |       Mean   Std. Dev.
-------------+-----------------------
  dumavoteUR |   .9316077      .25266
-------------------------------------

. 
. svy: mean q40aa 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =      1,553
Number of PSUs   =   1,553        Population size = 1,546.4234
                                  Design df       =      1,552

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       q40aa |   3.209326   .0255941      3.159123    3.259528
--------------------------------------------------------------

. estat sd

-------------------------------------
             |       Mean   Std. Dev.
-------------+-----------------------
       q40aa |   3.209326    .8209836
-------------------------------------

. 
. svy: mean q40ab 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =      1,486
Number of PSUs   =   1,486        Population size = 1,481.9695
                                  Design df       =      1,485

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       q40ab |   2.664228   .0293442      2.606667    2.721788
--------------------------------------------------------------

. estat sd

-------------------------------------
             |       Mean   Std. Dev.
-------------+-----------------------
       q40ab |   2.664228       .9399
-------------------------------------

. 
. svy: mean q5b
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =      1,573
Number of PSUs   =   1,573        Population size = 1,577.0587
                                  Design df       =      1,572

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         q5b |   .6295841   .0145021      .6011386    .6580296
--------------------------------------------------------------

. estat sd

-------------------------------------
             |       Mean   Std. Dev.
-------------+-----------------------
         q5b |   .6295841    .4830697
-------------------------------------

. 
. svy: mean dumavoteLDPR if pref_close_LDPR~=.
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        105
Number of PSUs   =     105        Population size = 111.591439
                                  Design df       =        104

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
dumavoteLDPR |   .5720898   .0539122        .46518    .6789997
--------------------------------------------------------------

. estat sd

-------------------------------------
             |       Mean   Std. Dev.
-------------+-----------------------
dumavoteLDPR |   .5720898    .4971488
-------------------------------------

. 
. svy: mean dumavotecomm if pref_close_comm~=.
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        136
Number of PSUs   =     136        Population size =  164.40559
                                  Design df       =        135

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
dumavotecomm |   .6845312    .043155      .5991839    .7698786
--------------------------------------------------------------

. estat sd

-------------------------------------
             |       Mean   Std. Dev.
-------------+-----------------------
dumavotecomm |   .6845312    .4664202
-------------------------------------

. 
. capture label drop party1

. label define party1 0 "Agreed nonpart's" 1 "Contested UR partisans" 2 "Agreed UR partisans"

. label values pref_close_ur party1

. 
. capture label drop party2

. label define party2 0 "Agreed nonpart's" 1 "Contested LDPR partisans" 2 "Agreed LDPR partisans"

. label values pref_close_LDPR party2

. 
. capture label drop party3

. label define party3 0 "Agreed nonpart's" 1 "Contested CPRF partisans" 2 "Agreed CPRF partisans"

. label values pref_close_comm party3

. 
. 
. 
. separate dumavoteUR, by(pref_close_ur)

              storage   display    value
variable name   type    format     label      variable label
-------------------------------------------------------------------------------------------------------------------------------------------------------------
dumavoteUR0     byte    %9.0g                 dumavoteUR, pref_close_ur == Agreed nonpart's
dumavoteUR1     byte    %9.0g                 dumavoteUR, pref_close_ur == Contested UR partisans
dumavoteUR2     byte    %9.0g                 dumavoteUR, pref_close_ur == Agreed UR partisans

. separate q40aa, by(pref_close_ur)

              storage   display    value
variable name   type    format     label      variable label
-------------------------------------------------------------------------------------------------------------------------------------------------------------
q40aa0          byte    %20.0g     q40aa      q40aa, pref_close_ur == Agreed nonpart's
q40aa1          byte    %20.0g     q40aa      q40aa, pref_close_ur == Contested UR partisans
q40aa2          byte    %20.0g     q40aa      q40aa, pref_close_ur == Agreed UR partisans

. separate q40ab, by(pref_close_ur)

              storage   display    value
variable name   type    format     label      variable label
-------------------------------------------------------------------------------------------------------------------------------------------------------------
q40ab0          byte    %20.0g     q40ab      q40ab, pref_close_ur == Agreed nonpart's
q40ab1          byte    %20.0g     q40ab      q40ab, pref_close_ur == Contested UR partisans
q40ab2          byte    %20.0g     q40ab      q40ab, pref_close_ur == Agreed UR partisans

. separate q5b, by(pref_close_ur)

              storage   display    value
variable name   type    format     label      variable label
-------------------------------------------------------------------------------------------------------------------------------------------------------------
q5b0            byte    %10.0g     q5b        q5b, pref_close_ur == Agreed nonpart's
q5b1            byte    %10.0g     q5b        q5b, pref_close_ur == Contested UR partisans
q5b2            byte    %10.0g     q5b        q5b, pref_close_ur == Agreed UR partisans

. 
.         *bar graphs
. graph bar (mean) dumavoteUR? [pweight=qvec], over(pref_close_ur, label(angle(0) labsize(small) alternate tick labgap(0)))  graphregion(color(white)) plotre
> gion(lcolor(black)) ///
> blabel(bar, format(%9.2f) size(small)) bar(1, color(white) lcolor(black)) bar(2, color(gs12)) bar(3, color(black)) title("1: Duma vote for UR") ytitle("") 
> ylab(, labsize(small))  legend(off) nofill

. graph save "`stick'\My Documents\Research\Paper PID Experiment\Russia\urgr1.gph", replace
(file C:\Users\Andy baker\OneDrive - University of Colorado at Boulder Office 365\My Documents\Research\Paper PID Experiment\Russia\urgr1.gph saved)

. 
. graph bar (mean) q40aa? [pweight=qvec], over(pref_close_ur, label(angle(0) labsize(small) alternate tick labgap(0)))  graphregion(color(white)) plotregion(
> lcolor(black)) ///
> blabel(bar, format(%9.2f) size(small)) bar(1, color(white) lcolor(black)) bar(2, color(gs12)) bar(3, color(black)) title("2: Putin job approval") exclude0 
> yscale(range(2(.5)4)) ytick(2 2.5 3 3.5 4) ylab(2 2.5 3 3.5 4) ytitle("") ylab(, labsize(small))  legend(off) nofill 

. graph save "`stick'\My Documents\Research\Paper PID Experiment\Russia\urgr2.gph", replace
(file C:\Users\Andy baker\OneDrive - University of Colorado at Boulder Office 365\My Documents\Research\Paper PID Experiment\Russia\urgr2.gph saved)

. 
. graph bar (mean) q40ab? [pweight=qvec], over(pref_close_ur, label(angle(0) labsize(small) alternate tick labgap(0)))  graphregion(color(white)) plotregion(
> lcolor(black)) ///
> blabel(bar, format(%9.2f) size(small)) bar(1, color(white) lcolor(black)) bar(2, color(gs12)) bar(3, color(black)) title("3: Government approval") exclude0
>  yscale(range(2(.5)4)) ytick(2 2.5 3 3.5 4) ylab(2 2.5 3 3.5 4) ytitle("") ylab(, labsize(small))  legend(off) nofill 

. graph save "`stick'\My Documents\Research\Paper PID Experiment\Russia\urgr3.gph", replace
(file C:\Users\Andy baker\OneDrive - University of Colorado at Boulder Office 365\My Documents\Research\Paper PID Experiment\Russia\urgr3.gph saved)

. 
. graph bar (mean) q5b? [pweight=qvec], over(pref_close_ur, label(angle(0) labsize(small) alternate  tick labgap(0)))  graphregion(color(white)) plotregion(l
> color(black)) ///
> blabel(bar, format(%9.2f) size(small)) bar(1, color(white) lcolor(black)) bar(2, color(gs12)) bar(3, color(black)) title("4: Medvedev job approval") yscale
> (range(0(.2)1)) ytick(0 .2 .4 .6 .8 1) ylab(0 .2 .4 .6 .8 1) ytitle("") ylab(, labsize(small))  legend(off) nofill 

. graph save "`stick'\My Documents\Research\Paper PID Experiment\Russia\urgr4.gph", replace
(file C:\Users\Andy baker\OneDrive - University of Colorado at Boulder Office 365\My Documents\Research\Paper PID Experiment\Russia\urgr4.gph saved)

. 
. graph combine ///
> "`stick'\My Documents\Research\Paper PID Experiment\Russia\urgr1.gph" ///
> "`stick'\My Documents\Research\Paper PID Experiment\Russia\urgr2.gph" ///
> "`stick'\My Documents\Research\Paper PID Experiment\Russia\urgr3.gph" ///
> "`stick'\My Documents\Research\Paper PID Experiment\Russia\urgr4.gph", ///
> row(2) col(2) graphregion(color(white))

. graph export "`stick'\My Documents\Research\Paper PID Experiment\LaTeX\RussiaCriterion1.pdf", as(pdf) replace
(file C:\Users\Andy baker\OneDrive - University of Colorado at Boulder Office 365\My Documents\Research\Paper PID Experiment\LaTeX\RussiaCriterion1.pdf writt
> en in PDF format)

. 
. drop q40aa0-q5b2

. separate dumavoteLDPR, by(pref_close_LDPR)

              storage   display    value
variable name   type    format     label      variable label
-------------------------------------------------------------------------------------------------------------------------------------------------------------
dumavoteLDPR0   byte    %9.0g                 dumavoteLDPR, pref_close_LDPR == Agreed nonpart's
dumavoteLDPR1   byte    %9.0g                 dumavoteLDPR, pref_close_LDPR == Contested LDPR partisans
dumavoteLDPR2   byte    %9.0g                 dumavoteLDPR, pref_close_LDPR == Agreed LDPR partisans

. separate q40aa, by(pref_close_LDPR)

              storage   display    value
variable name   type    format     label      variable label
-------------------------------------------------------------------------------------------------------------------------------------------------------------
q40aa0          byte    %20.0g     q40aa      q40aa, pref_close_LDPR == Agreed nonpart's
q40aa1          byte    %20.0g     q40aa      q40aa, pref_close_LDPR == Contested LDPR partisans
q40aa2          byte    %20.0g     q40aa      q40aa, pref_close_LDPR == Agreed LDPR partisans

. separate q40ab, by(pref_close_LDPR)

              storage   display    value
variable name   type    format     label      variable label
-------------------------------------------------------------------------------------------------------------------------------------------------------------
q40ab0          byte    %20.0g     q40ab      q40ab, pref_close_LDPR == Agreed nonpart's
q40ab1          byte    %20.0g     q40ab      q40ab, pref_close_LDPR == Contested LDPR partisans
q40ab2          byte    %20.0g     q40ab      q40ab, pref_close_LDPR == Agreed LDPR partisans

. separate q5b, by(pref_close_LDPR)

              storage   display    value
variable name   type    format     label      variable label
-------------------------------------------------------------------------------------------------------------------------------------------------------------
q5b0            byte    %10.0g     q5b        q5b, pref_close_LDPR == Agreed nonpart's
q5b1            byte    %10.0g     q5b        q5b, pref_close_LDPR == Contested LDPR partisans
q5b2            byte    %10.0g     q5b        q5b, pref_close_LDPR == Agreed LDPR partisans

. 
. graph bar (mean) dumavoteLDPR? [pweight=qvec], over(pref_close_LDPR, label(angle(0) labsize(small) alternate tick labgap(0)))  graphregion(color(white)) pl
> otregion(lcolor(black)) ///
> blabel(bar, format(%9.2f) size(small)) bar(1, color(white) lcolor(black)) bar(2, color(gs12)) bar(3, color(black)) title("1: Duma vote for LDPR") ytitle(""
> ) ylab(, labsize(small))  legend(off) nofill

. graph save "`stick'\My Documents\Research\Paper PID Experiment\Russia\ldgr1.gph", replace
(file C:\Users\Andy baker\OneDrive - University of Colorado at Boulder Office 365\My Documents\Research\Paper PID Experiment\Russia\ldgr1.gph saved)

. 
. graph bar (mean) q40aa? [pweight=qvec], over(pref_close_LDPR, label(angle(0) labsize(small) alternate tick labgap(0)))  graphregion(color(white)) plotregio
> n(lcolor(black)) ///
> blabel(bar, format(%9.2f) size(small)) bar(1, color(white) lcolor(black)) bar(2, color(gs12)) bar(3, color(black)) title("2: Putin job approval") exclude0 
> yscale(range(2(.5)4)) ytick(2 2.5 3 3.5 4) ylab(2 2.5 3 3.5 4) ytitle("") ylab(, labsize(small))  legend(off) nofill 

. graph save "`stick'\My Documents\Research\Paper PID Experiment\Russia\ldgr2.gph", replace
(file C:\Users\Andy baker\OneDrive - University of Colorado at Boulder Office 365\My Documents\Research\Paper PID Experiment\Russia\ldgr2.gph saved)

. 
. graph bar (mean) q40ab? [pweight=qvec], over(pref_close_LDPR, label(angle(0) labsize(small) alternate tick labgap(0)))  graphregion(color(white)) plotregio
> n(lcolor(black)) ///
> blabel(bar, format(%9.2f) size(small)) bar(1, color(white) lcolor(black)) bar(2, color(gs12)) bar(3, color(black)) title("3: Government approval") exclude0
>  yscale(range(2(.5)4)) ytick(2 2.5 3 3.5 4) ylab(2 2.5 3 3.5 4) ytitle("") ylab(, labsize(small))  legend(off) nofill 

. graph save "`stick'\My Documents\Research\Paper PID Experiment\Russia\ldgr3.gph", replace
(file C:\Users\Andy baker\OneDrive - University of Colorado at Boulder Office 365\My Documents\Research\Paper PID Experiment\Russia\ldgr3.gph saved)

. 
. graph bar (mean) q5b? [pweight=qvec], over(pref_close_LDPR, label(angle(0) labsize(small) alternate  tick labgap(0)))  graphregion(color(white)) plotregion
> (lcolor(black)) ///
> blabel(bar, format(%9.2f) size(small)) bar(1, color(white) lcolor(black)) bar(2, color(gs12)) bar(3, color(black)) title("4: Medvedev job approval") yscale
> (range(0(.2)1)) ytick(0 .2 .4 .6 .8 1) ylab(0 .2 .4 .6 .8 1) ytitle("") ylab(, labsize(small))  legend(off) nofill 

. graph save "`stick'\My Documents\Research\Paper PID Experiment\Russia\ldgr4.gph", replace
(file C:\Users\Andy baker\OneDrive - University of Colorado at Boulder Office 365\My Documents\Research\Paper PID Experiment\Russia\ldgr4.gph saved)

. 
. graph combine ///
> "`stick'\My Documents\Research\Paper PID Experiment\Russia\ldgr1.gph" ///
> "`stick'\My Documents\Research\Paper PID Experiment\Russia\ldgr2.gph" ///
> "`stick'\My Documents\Research\Paper PID Experiment\Russia\ldgr3.gph" ///
> "`stick'\My Documents\Research\Paper PID Experiment\Russia\ldgr4.gph", ///
> row(2) col(2) graphregion(color(white))

. graph export "`stick'\My Documents\Research\Paper PID Experiment\LaTeX\RussiaCriterion2.pdf", as(pdf) replace
(file C:\Users\Andy baker\OneDrive - University of Colorado at Boulder Office 365\My Documents\Research\Paper PID Experiment\LaTeX\RussiaCriterion2.pdf writt
> en in PDF format)

. 
. drop q40aa0-q5b2

. separate dumavotecomm, by(pref_close_comm)

              storage   display    value
variable name   type    format     label      variable label
-------------------------------------------------------------------------------------------------------------------------------------------------------------
dumavotecomm0   byte    %9.0g                 dumavotecomm, pref_close_comm == Agreed nonpart's
dumavotecomm1   byte    %9.0g                 dumavotecomm, pref_close_comm == Contested CPRF partisans
dumavotecomm2   byte    %9.0g                 dumavotecomm, pref_close_comm == Agreed CPRF partisans

. separate q40aa, by(pref_close_comm)

              storage   display    value
variable name   type    format     label      variable label
-------------------------------------------------------------------------------------------------------------------------------------------------------------
q40aa0          byte    %20.0g     q40aa      q40aa, pref_close_comm == Agreed nonpart's
q40aa1          byte    %20.0g     q40aa      q40aa, pref_close_comm == Contested CPRF partisans
q40aa2          byte    %20.0g     q40aa      q40aa, pref_close_comm == Agreed CPRF partisans

. separate q40ab, by(pref_close_comm)

              storage   display    value
variable name   type    format     label      variable label
-------------------------------------------------------------------------------------------------------------------------------------------------------------
q40ab0          byte    %20.0g     q40ab      q40ab, pref_close_comm == Agreed nonpart's
q40ab1          byte    %20.0g     q40ab      q40ab, pref_close_comm == Contested CPRF partisans
q40ab2          byte    %20.0g     q40ab      q40ab, pref_close_comm == Agreed CPRF partisans

. separate q5b, by(pref_close_comm)

              storage   display    value
variable name   type    format     label      variable label
-------------------------------------------------------------------------------------------------------------------------------------------------------------
q5b0            byte    %10.0g     q5b        q5b, pref_close_comm == Agreed nonpart's
q5b1            byte    %10.0g     q5b        q5b, pref_close_comm == Contested CPRF partisans
q5b2            byte    %10.0g     q5b        q5b, pref_close_comm == Agreed CPRF partisans

. 
. graph bar (mean) dumavotecomm? [pweight=qvec], over(pref_close_comm, label(angle(0) labsize(small) alternate tick labgap(0)))  graphregion(color(white)) pl
> otregion(lcolor(black)) ///
> blabel(bar, format(%9.2f) size(small)) bar(1, color(white) lcolor(black)) bar(2, color(gs12)) bar(3, color(black)) title("1: Duma vote for CPRF") ytitle(""
> ) ylab(, labsize(small))  legend(off) nofill

. graph save "`stick'\My Documents\Research\Paper PID Experiment\Russia\cpgr1.gph", replace
(file C:\Users\Andy baker\OneDrive - University of Colorado at Boulder Office 365\My Documents\Research\Paper PID Experiment\Russia\cpgr1.gph saved)

. 
. graph bar (mean) q40aa? [pweight=qvec], over(pref_close_comm, label(angle(0) labsize(small) alternate tick labgap(0)))  graphregion(color(white)) plotregio
> n(lcolor(black)) ///
> blabel(bar, format(%9.2f) size(small)) bar(1, color(white) lcolor(black)) bar(2, color(gs12)) bar(3, color(black)) title("2: Putin job approval") exclude0 
> yscale(range(2(.5)4)) ytick(2 2.5 3 3.5 4) ylab(2 2.5 3 3.5 4) ytitle("") ylab(, labsize(small))  legend(off) nofill 

. graph save "`stick'\My Documents\Research\Paper PID Experiment\Russia\cpgr2.gph", replace
(file C:\Users\Andy baker\OneDrive - University of Colorado at Boulder Office 365\My Documents\Research\Paper PID Experiment\Russia\cpgr2.gph saved)

. 
. graph bar (mean) q40ab? [pweight=qvec], over(pref_close_comm, label(angle(0) labsize(small) alternate tick labgap(0)))  graphregion(color(white)) plotregio
> n(lcolor(black)) ///
> blabel(bar, format(%9.2f) size(small)) bar(1, color(white) lcolor(black)) bar(2, color(gs12)) bar(3, color(black)) title("3: Government approval") exclude0
>  yscale(range(2(.5)4)) ytick(2 2.5 3 3.5 4) ylab(2 2.5 3 3.5 4) ytitle("") ylab(, labsize(small))  legend(off) nofill 

. graph save "`stick'\My Documents\Research\Paper PID Experiment\Russia\cpgr3.gph", replace
(file C:\Users\Andy baker\OneDrive - University of Colorado at Boulder Office 365\My Documents\Research\Paper PID Experiment\Russia\cpgr3.gph saved)

. 
. graph bar (mean) q5b? [pweight=qvec], over(pref_close_comm, label(angle(0) labsize(small) alternate  tick labgap(0)))  graphregion(color(white)) plotregion
> (lcolor(black)) ///
> blabel(bar, format(%9.2f) size(small)) bar(1, color(white) lcolor(black)) bar(2, color(gs12)) bar(3, color(black)) title("4: Medvedev job approval") yscale
> (range(0(.2)1)) ytick(0 .2 .4 .6 .8 1) ylab(0 .2 .4 .6 .8 1) ytitle("") ylab(, labsize(small))  legend(off) nofill 

. graph save "`stick'\My Documents\Research\Paper PID Experiment\Russia\cpgr4.gph", replace
(file C:\Users\Andy baker\OneDrive - University of Colorado at Boulder Office 365\My Documents\Research\Paper PID Experiment\Russia\cpgr4.gph saved)

. 
. graph combine ///
> "`stick'\My Documents\Research\Paper PID Experiment\Russia\cpgr1.gph" ///
> "`stick'\My Documents\Research\Paper PID Experiment\Russia\cpgr2.gph" ///
> "`stick'\My Documents\Research\Paper PID Experiment\Russia\cpgr3.gph" ///
> "`stick'\My Documents\Research\Paper PID Experiment\Russia\cpgr4.gph", ///
> row(2) col(2) graphregion(color(white))

. graph export "`stick'\My Documents\Research\Paper PID Experiment\LaTeX\RussiaCriterion3.pdf", as(pdf) replace
(file C:\Users\Andy baker\OneDrive - University of Colorado at Boulder Office 365\My Documents\Research\Paper PID Experiment\LaTeX\RussiaCriterion3.pdf writt
> en in PDF format)

. 
. *VII. IRT MODELS
. *Note: I often fed starting values to these IRT models but it never seemed to make a difference. What does make a difference is number of integration point
> s and the integration method.
. *More intpoints are more accurate (Ayala 2009, p. 75 and 104), and ghermite is not trustworthy (see "semintro12" Stata manual, p. 6). Models using ghermite
>  would often converge but were very unstable/sensitive and often nonsensical SEs. 
. *mcaghermite produced stable results repeatedly, although slow. mvaghermite also stable and trustworthy, but slightly less likely to converge
. *Small parties are more challenging to estimate because of skew (Ayala 2009, 104), so I did these with 60 intpoints
. 
.         *1: UR
. svy: irt grm prefexp_ur prefimp_ur identity_ur close_ur dumavote_ur ft_ur , intpoints(30) difficult intmethod(mcaghermite) 
(running irt on estimation sample)

Survey: Graded response model

Number of strata   =         1                  Number of obs     =      1,602
Number of PSUs     =     1,602                  Population size   =      1,602
                                                Design df         =      1,601

------------------------------------------------------------------------------
             |             Linearized
             |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
prefexp_ur   |
     Discrim |   8.971398   3.594358     2.50   0.013     1.921256    16.02154
        Diff |
         =1  |   .2267572   .0506002     4.48   0.000     .1275077    .3260068
-------------+----------------------------------------------------------------
prefimp_ur   |
     Discrim |   5.933377   1.140556     5.20   0.000     3.696236    8.170517
        Diff |
         =1  |    .210711    .046815     4.50   0.000     .1188858    .3025362
-------------+----------------------------------------------------------------
identity_ur  |
     Discrim |   4.990242   1.557149     3.20   0.001     1.935978    8.044506
        Diff |
         =1  |   .2762323   .0585057     4.72   0.000     .1614765    .3909882
-------------+----------------------------------------------------------------
close_ur     |
     Discrim |   3.809661   .3801524    10.02   0.000     3.064013     4.55531
        Diff |
         =1  |   .6752688   .0424803    15.90   0.000      .591946    .7585916
-------------+----------------------------------------------------------------
dumavote_ur  |
     Discrim |   5.368919   .6155672     8.72   0.000     4.161517    6.576322
        Diff |
         =1  |   .2891175    .038417     7.53   0.000     .2137646    .3644704
-------------+----------------------------------------------------------------
ft_ur        |
     Discrim |    2.88117   .2015798    14.29   0.000     2.485782    3.276558
        Diff |
       >= 1  |  -1.283522   .0744542   -17.24   0.000     -1.42956   -1.137484
       >= 2  |   -1.03343   .0660409   -15.65   0.000    -1.162965   -.9038939
       >= 3  |   -.892681   .0621622   -14.36   0.000    -1.014609   -.7707531
       >= 4  |  -.6514785   .0570091   -11.43   0.000    -.7632988   -.5396583
       >= 5  |   -.552143   .0548147   -10.07   0.000    -.6596591   -.4446269
       >= 6  |   .0281232   .0460448     0.61   0.541    -.0621911    .1184376
       >= 7  |    .304104    .043761     6.95   0.000     .2182691    .3899389
       >= 8  |   .5486346   .0445051    12.33   0.000     .4613403    .6359289
       >= 9  |   .7878225   .0470604    16.74   0.000     .6955161    .8801289
        =10  |   .9647134   .0510067    18.91   0.000     .8646665     1.06476
------------------------------------------------------------------------------

. test _b[prefexp_ur:Theta]=_b[identity_ur:Theta]

Adjusted Wald test

 ( 1)  [prefexp_ur]Theta - [identity_ur]Theta = 0

       F(  1,  1601) =    1.06
            Prob > F =    0.3045

. test _b[prefexp_ur:Theta]=_b[close_ur:Theta]

Adjusted Wald test

 ( 1)  [prefexp_ur]Theta - [close_ur]Theta = 0

       F(  1,  1601) =    1.98
            Prob > F =    0.1597

. test _b[prefimp_ur:Theta]=_b[identity_ur:Theta]

Adjusted Wald test

 ( 1)  [prefimp_ur]Theta - [identity_ur]Theta = 0

       F(  1,  1601) =    0.24
            Prob > F =    0.6236

. test _b[prefimp_ur:Theta]=_b[close_ur:Theta]

Adjusted Wald test

 ( 1)  [prefimp_ur]Theta - [close_ur]Theta = 0

       F(  1,  1601) =    3.06
            Prob > F =    0.0806

. predict thetaur, latent
(option ebmeans assumed)
(using 30 quadrature points)

. summ thetaur

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     thetaur |      1,602     .095235    .8897745  -1.395134   1.465681

. 
. irtgraph icc (ft_ur, lcolor(gs14) lwidth(vthin) lpattern(dash))  (dumavote_ur, lcolor(black) lwidth(medium) lpattern(dash)) (close_ur identity_ur prefexp_u
> r prefimp_ur, lcolor(black) lwidth(medthick)), ///
> addplot((scatteri .4 .27 "Duma Vote", msymbol(i) mlabpos(0) mlabangle(74) mlabcolor(black) mlabsize(vsmall)) ///
> (scatteri .5 .62 "Close({it:P{superscript:0}N{superscript:1}})", msymbol(i) mlabpos(0) mlabangle(69) mlabcolor(black) mlabsize(small)) ///
> (scatteri .4 0 "Prefer({it:P{superscript:0}N{superscript:0}})", msymbol(i) mlabpos(0) mlabangle(73) mlabcolor(black) mlabsize(small)) ///
> (scatteri .86 .36 "Prefer({it:P{superscript:1}N{superscript:0}})", msymbol(i) mlabpos(0) mlabangle(72) mlabcolor(black) mlabsize(small)) /// 
> (scatteri .86 .84 "Belong({it:P{superscript:1}N{superscript:1}})", msymbol(i) mlabpos(0) mlabangle(47) mlabcolor(black) mlabsize(small))) ///
> bcc range(-1.40 1.47) legend(off) graphregion(color(white)) plotregion(lstyle(yxline) lcolor(black)) b1title("{it: θ{subscript:UR}}: Strength of United Rus
> sia Partisanship") xtitle("") ytitle("{it:Pr}({it:x{subscript:j•UR}}|{it: θ{subscript:UR}})") title("")  xtick(-2(.5)2) xlabel(-2(1)2)

. *graph export "F:\My Documents\Research\Paper PID Experiment\LaTeX\URIRT.pdf", as(pdf) replace
. 
. polychoric thetaur prefexp_ur prefimp_ur identity_ur close_ur dumavote_ur ft_ur, pw verbose

Variables :  thetaur prefexp_ur
Type :       polyserial
Rho        = .96837166
S.e.       = .00551431

Variables :  thetaur prefimp_ur
Type :       polyserial
Rho        = .96544412
S.e.       = .00552848

Variables :  thetaur identity_ur
Type :       polyserial
Rho        = .95648689
S.e.       = .00802173

Variables :  thetaur close_ur
Type :       polyserial
Rho        = .92192111
S.e.       = .00676102

Variables :  thetaur dumavote_ur
Type :       polyserial
Rho        = .9519435
S.e.       = .00378917

Variables :  thetaur ft_ur
Type :       Pearson
Rho        = .90604432
S.e.       = .01168764
no observations

Variables :  prefexp_ur prefimp_ur
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .
no observations

Variables :  prefexp_ur identity_ur
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefexp_ur close_ur
Type :       polychoric
Rho        = .8212842
S.e.       = .0364693
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefexp_ur dumavote_ur
Type :       polychoric
Rho        = .92161574
S.e.       = .01954098
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefexp_ur ft_ur
Type :       polyserial
Rho        = .80379467
S.e.       = .02620443
no observations

Variables :  prefimp_ur identity_ur
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefimp_ur close_ur
Type :       polychoric
Rho        = .86801036
S.e.       = .02427947
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefimp_ur dumavote_ur
Type :       polychoric
Rho        = .91535267
S.e.       = .01647016
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefimp_ur ft_ur
Type :       polyserial
Rho        = .80171296
S.e.       = .02073958

Variables :  identity_ur close_ur
Type :       polychoric
Rho        = .89545971
S.e.       = .02600968
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  identity_ur dumavote_ur
Type :       polychoric
Rho        = .90311131
S.e.       = .02329953
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  identity_ur ft_ur
Type :       polyserial
Rho        = .81757811
S.e.       = .03051162

Variables :  close_ur dumavote_ur
Type :       polychoric
Rho        = .87027994
S.e.       = .01546229
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  close_ur ft_ur
Type :       polyserial
Rho        = .7331594
S.e.       = .02049792

Variables :  dumavote_ur ft_ur
Type :       polyserial
Rho        = .76149248
S.e.       = .01546813

. 
.         *2: LDPR
. set more off

. *intpoints of 60 more stable here for small parties
. svy: irt grm prefexp_LDPR prefimp_LDPR identity_LDPR close_LDPR dumavote_LDPR ft_LDPR , intpoints(60) difficult intmethod(mcaghermite)
(running irt on estimation sample)

Survey: Graded response model

Number of strata   =         1                  Number of obs     =      1,602
Number of PSUs     =     1,602                  Population size   =      1,602
                                                Design df         =      1,601

------------------------------------------------------------------------------
             |             Linearized
             |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
prefexp_LDPR |
     Discrim |   8.414775   5.064076     1.66   0.097    -1.518142    18.34769
        Diff |
         =1  |   1.514167   .0853637    17.74   0.000     1.346731    1.681603
-------------+----------------------------------------------------------------
prefimp_LDPR |
     Discrim |   8.726166   4.468584     1.95   0.051    -.0387239    17.49106
        Diff |
         =1  |   1.496122   .0671869    22.27   0.000     1.364338    1.627906
-------------+----------------------------------------------------------------
identity_L~R |
     Discrim |   8.230337   4.805176     1.71   0.087     -1.19476    17.65543
        Diff |
         =1  |   1.549924   .0857857    18.07   0.000      1.38166    1.718188
-------------+----------------------------------------------------------------
close_LDPR   |
     Discrim |   7.414194    1.96309     3.78   0.000     3.563697    11.26469
        Diff |
         =1  |     1.7307   .0736856    23.49   0.000      1.58617    1.875231
-------------+----------------------------------------------------------------
dumavote_L~R |
     Discrim |   8.820661   3.385641     2.61   0.009     2.179905    15.46142
        Diff |
         =1  |   1.599812   .0655382    24.41   0.000     1.471263    1.728362
-------------+----------------------------------------------------------------
ft_LDPR      |
     Discrim |   2.071866   .2580996     8.03   0.000     1.565617    2.578115
        Diff |
       >= 1  |  -.8770635   .0758845   -11.56   0.000    -1.025907   -.7282202
       >= 2  |  -.4508904   .0606293    -7.44   0.000    -.5698115   -.3319694
       >= 3  |   -.185901   .0551323    -3.37   0.001    -.2940401    -.077762
       >= 4  |   .0967388    .053149     1.82   0.069    -.0075102    .2009878
       >= 5  |    .306062   .0546053     5.60   0.000     .1989566    .4131675
       >= 6  |    1.31189   .0912849    14.37   0.000      1.13284    1.490941
       >= 7  |   1.611224   .1094231    14.72   0.000     1.396596    1.825852
       >= 8  |   1.885195    .128462    14.68   0.000     1.633223    2.137166
       >= 9  |   2.101802   .1465772    14.34   0.000     1.814298    2.389305
        =10  |   2.275055   .1602221    14.20   0.000     1.960788    2.589323
------------------------------------------------------------------------------

. test _b[prefexp_LDPR:Theta]=_b[identity_LDPR:Theta]

Adjusted Wald test

 ( 1)  [prefexp_LDPR]Theta - [identity_LDPR]Theta = 0

       F(  1,  1601) =    0.00
            Prob > F =    0.9774

. test _b[prefexp_LDPR:Theta]=_b[close_LDPR:Theta]

Adjusted Wald test

 ( 1)  [prefexp_LDPR]Theta - [close_LDPR]Theta = 0

       F(  1,  1601) =    0.03
            Prob > F =    0.8612

. test _b[prefimp_LDPR:Theta]=_b[identity_LDPR:Theta]

Adjusted Wald test

 ( 1)  [prefimp_LDPR]Theta - [identity_LDPR]Theta = 0

       F(  1,  1601) =    0.01
            Prob > F =    0.9382

. test _b[prefimp_LDPR:Theta]=_b[close_LDPR:Theta]

Adjusted Wald test

 ( 1)  [prefimp_LDPR]Theta - [close_LDPR]Theta = 0

       F(  1,  1601) =    0.07
            Prob > F =    0.7985

. predict thetaLDPR, latent
(option ebmeans assumed)
(using 60 quadrature points)

. summ thetaLDPR

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   thetaLDPR |      1,602   -.0267919    .7018368  -.9732381   2.315963

. 
. irtgraph icc (ft_LDPR, lcolor(gs14) lwidth(vthin) lpattern(dash))  (dumavote_LDPR, lcolor(black) lwidth(medium) lpattern(dash)) (close_LDPR identity_LDPR p
> refexp_LDPR prefimp_LDPR, lcolor(black) lwidth(medthick)), ///
> addplot((scatteri .85 1.55 "Prefer({it:P{superscript:1}N{superscript:0}})", msymbol(i) mlabpos(0) mlabangle(75) mlabcolor(black) mlabsize(small)) ///
> (scatteri .5 1.65 "Duma Vote", msymbol(i) mlabpos(0) mlabangle(81) mlabcolor(black) mlabsize(vsmall)) ///
> (scatteri .5 1.84 "Close({it:P{superscript:0}N{superscript:1}})", msymbol(i) mlabpos(0) mlabangle(80) mlabcolor(black) mlabsize(small)) ///
> (scatteri .5 1.41 "Prefer({it:P{superscript:0}N{superscript:0}})", msymbol(i) mlabpos(0) mlabangle(81) mlabcolor(black) mlabsize(small)) /// 
> (scatteri .8 2.1 "Belong({it:P{superscript:1}N{superscript:1}})", msymbol(i) mlabpos(0) mlabangle(77) mlabcolor(black) mlabsize(small))) ///
> bcc range(-.98 2.32) legend(off) graphregion(color(white)) plotregion(lstyle(yxline) lcolor(black)) b1title("{it: θ{subscript:LDPR}}: Strength of LDPR Part
> isanship") xtitle("") ytitle("{it:Pr}({it:x{subscript:j•LDPR}}|{it: θ{subscript:LDPR}})")  title("") xtick(-2(.5)2.5) xlabel(-2(1)2.5)

. *graph export "F:\My Documents\Research\Paper PID Experiment\LaTeX\LDPRIRT.pdf", as(pdf) replace
. 
. polychoric thetaLDPR prefexp_LDPR prefimp_LDPR identity_LDPR close_LDPR dumavote_LDPR ft_LDPR, pw verbose

Variables :  thetaLDPR prefexp_LDPR
Type :       polyserial
Rho        = .90847976
S.e.       = .02447989

Variables :  thetaLDPR prefimp_LDPR
Type :       polyserial
Rho        = .91262819
S.e.       = .01921688

Variables :  thetaLDPR identity_LDPR
Type :       polyserial
Rho        = .8994228
S.e.       = .02434092

Variables :  thetaLDPR close_LDPR
Type :       polyserial
Rho        = .82141807
S.e.       = .01598419

Variables :  thetaLDPR dumavote_LDPR
Type :       polyserial
Rho        = .88322739
S.e.       = .01356647

Variables :  thetaLDPR ft_LDPR
Type :       Pearson
Rho        = .90469558
S.e.       = .01212372
no observations

Variables :  prefexp_LDPR prefimp_LDPR
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .
no observations

Variables :  prefexp_LDPR identity_LDPR
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefexp_LDPR close_LDPR
Type :       polychoric
Rho        = .92833843
S.e.       = .04094724
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefexp_LDPR dumavote_LDPR
Type :       polychoric
Rho        = .9601187
S.e.       = .02300362
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefexp_LDPR ft_LDPR
Type :       polyserial
Rho        = .78294249
S.e.       = .04510899
no observations

Variables :  prefimp_LDPR identity_LDPR
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefimp_LDPR close_LDPR
Type :       polychoric
Rho        = .95994603
S.e.       = .0183329
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefimp_LDPR dumavote_LDPR
Type :       polychoric
Rho        = .97752996
S.e.       = .01158773
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefimp_LDPR ft_LDPR
Type :       polyserial
Rho        = .63783649
S.e.       = .06432592

Variables :  identity_LDPR close_LDPR
Type :       polychoric
Rho        = .95872342
S.e.       = .03009869
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  identity_LDPR dumavote_LDPR
Type :       polychoric
Rho        = .90098797
S.e.       = .04814706
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  identity_LDPR ft_LDPR
Type :       polyserial
Rho        = .7814352
S.e.       = .0616839

Variables :  close_LDPR dumavote_LDPR
Type :       polychoric
Rho        = .95312674
S.e.       = .01512823
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  close_LDPR ft_LDPR
Type :       polyserial
Rho        = .70552642
S.e.       = .04188898

Variables :  dumavote_LDPR ft_LDPR
Type :       polyserial
Rho        = .64971305
S.e.       = .04280245

. 
.         *3: COMM
. set more off

. display "$S_TIME  $S_DATE"
20:44:20   6 Mar 2018

. 
. irt grm pref_comm identity_comm close_comm dumavote_comm ft_comm , intpoints(60) difficult intmethod(mcaghermite)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3889.4635  
Iteration 1:   log likelihood = -3889.4635  (backed up)

Fitting full model:

Iteration 0:   log likelihood = -3637.3627  
Iteration 1:   log likelihood = -3286.0318  
Iteration 2:   log likelihood =  -3218.582  
Iteration 3:   log likelihood = -3209.8253  
Iteration 4:   log likelihood = -3207.3159  
Iteration 5:   log likelihood =  -3207.282  
Iteration 6:   log likelihood = -3207.2818  

Graded response model                           Number of obs     =      1,602
Log likelihood = -3207.2818
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
pref_comm    |
     Discrim |   10.37156   2.848524     3.64   0.000     4.788559    15.95457
        Diff |
         =1  |     1.3714   .0610406    22.47   0.000     1.251763    1.491038
-------------+----------------------------------------------------------------
identity_c~m |
     Discrim |   7.949796    3.09475     2.57   0.010     1.884197     14.0154
        Diff |
         =1  |    1.51094   .0803151    18.81   0.000     1.353525    1.668354
-------------+----------------------------------------------------------------
close_comm   |
     Discrim |   5.131154   .6876412     7.46   0.000     3.783402    6.478906
        Diff |
         =1  |   1.605177   .0661322    24.27   0.000      1.47556    1.734794
-------------+----------------------------------------------------------------
dumavote_c~m |
     Discrim |   9.183281   2.091005     4.39   0.000     5.084987    13.28158
        Diff |
         =1  |   1.446608    .056736    25.50   0.000     1.335408    1.557809
-------------+----------------------------------------------------------------
ft_comm      |
     Discrim |    2.65023   .2214715    11.97   0.000     2.216153    3.084306
        Diff |
       >= 1  |  -.9725245   .0560496   -17.35   0.000     -1.08238   -.8626692
       >= 2  |  -.6106747   .0488539   -12.50   0.000    -.7064266   -.5149228
       >= 3  |  -.3723709   .0455923    -8.17   0.000    -.4617302   -.2830117
       >= 4  |  -.1067136   .0434339    -2.46   0.014    -.1918424   -.0215848
       >= 5  |    .127314    .042999     2.96   0.003     .0430376    .2115904
       >= 6  |   1.007079   .0555858    18.12   0.000     .8981326    1.116025
       >= 7  |   1.277586   .0642907    19.87   0.000     1.151578    1.403593
       >= 8  |   1.480048   .0721325    20.52   0.000     1.338671    1.621425
       >= 9  |    1.71116   .0824311    20.76   0.000     1.549598    1.872722
        =10  |   1.827624   .0882489    20.71   0.000     1.654659    2.000588
------------------------------------------------------------------------------

. 
. test _b[pref_comm:Theta]=_b[identity_comm:Theta]

 ( 1)  [pref_comm]Theta - [identity_comm]Theta = 0

           chi2(  1) =    0.35
         Prob > chi2 =    0.5538

. test _b[pref_comm:Theta]=_b[close_comm:Theta]

 ( 1)  [pref_comm]Theta - [close_comm]Theta = 0

           chi2(  1) =    3.23
         Prob > chi2 =    0.0721

. predict thetacomm, latent ebmodes

. summ thetacomm

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   thetacomm |      1,602    .0763175    .7835108  -1.101251   1.978675

. 
. irtgraph icc (ft_comm, lcolor(gs14) lwidth(vthin) lpattern(dash))  (dumavote_comm, lcolor(black) lwidth(medium) lpattern(dash)) (close_comm identity_comm p
> ref_comm, lcolor(black) lwidth(medthick)), ///
> addplot((scatteri .5 1.7 "Close({it:P{superscript:0}N{superscript:1}})", msymbol(i) mlabpos(0) mlabangle(73) mlabcolor(black) mlabsize(small)) ///
> (scatteri .8 1.56 "Duma Vote", msymbol(i) mlabpos(0) mlabangle(77) mlabcolor(black) mlabsize(vsmall)) ///
> (scatteri .5 1.31 "Prefer({it:N{superscript:0}})", msymbol(i) mlabpos(0) mlabangle(82) mlabcolor(black) mlabsize(small)) /// 
> (scatteri .85 1.85 "Belong({it:P{superscript:1}N{superscript:1}})", msymbol(i) mlabpos(0) mlabangle(60) mlabcolor(black) mlabsize(small))) ///
> bcc range(-1.11 1.98) legend(off) graphregion(color(white)) plotregion(lstyle(yxline) lcolor(black)) b1title("{it: θ{subscript:CPRF}}: Strength of CPRF Par
> tisanship") xtitle("") ytitle("{it:Pr}({it:x{subscript:j•CPRF}}|{it: θ{subscript:CPRF}})")  title("") xtick(-2(.5)2) xlabel(-2(1)2) 

. *graph export "F:\My Documents\Research\Paper PID Experiment\LaTeX\CPRFIRT.pdf", as(pdf) replace
. 
. polychoric thetacomm pref_comm identity_comm close_comm dumavote_comm ft_comm , pw verbose 

Variables :  thetacomm pref_comm
Type :       polyserial
Rho        = .96284418
S.e.       = .00847802

Variables :  thetacomm identity_comm
Type :       polyserial
Rho        = .94886229
S.e.       = .015799

Variables :  thetacomm close_comm
Type :       polyserial
Rho        = .90733286
S.e.       = .01006791

Variables :  thetacomm dumavote_comm
Type :       polyserial
Rho        = .95361564
S.e.       = .00717875

Variables :  thetacomm ft_comm
Type :       Pearson
Rho        = .9558601
S.e.       = .00836764
no observations

Variables :  pref_comm identity_comm
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  pref_comm close_comm
Type :       polychoric
Rho        = .94455458
S.e.       = .0158688
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  pref_comm dumavote_comm
Type :       polychoric
Rho        = .97501904
S.e.       = .00858681
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  pref_comm ft_comm
Type :       polyserial
Rho        = .79805026
S.e.       = .03113275

Variables :  identity_comm close_comm
Type :       polychoric
Rho        = .91005196
S.e.       = .04013186
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  identity_comm dumavote_comm
Type :       polychoric
Rho        = .95947516
S.e.       = .02141671
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  identity_comm ft_comm
Type :       polyserial
Rho        = .81934814
S.e.       = .03779777

Variables :  close_comm dumavote_comm
Type :       polychoric
Rho        = .94188488
S.e.       = .01417307
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  close_comm ft_comm
Type :       polyserial
Rho        = .74091472
S.e.       = .03052289

Variables :  dumavote_comm ft_comm
Type :       polyserial
Rho        = .77088813
S.e.       = .02799207

. 
. display "$S_TIME  $S_DATE"
20:45:19   6 Mar 2018

. 
. *Item-rest correlations
. *1: UR
. capture drop thetaur

. svy: irt grm prefimp_ur identity_ur close_ur dumavote_ur ft_ur , intpoints(30) difficult intmethod(mcaghermite) 
(running irt on estimation sample)

Survey: Graded response model

Number of strata   =         1                  Number of obs     =      1,602
Number of PSUs     =     1,602                  Population size   =      1,602
                                                Design df         =      1,601

------------------------------------------------------------------------------
             |             Linearized
             |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
prefimp_ur   |
     Discrim |   5.940941   1.153454     5.15   0.000     3.678503     8.20338
        Diff |
         =1  |   .2085867   .0466973     4.47   0.000     .1169924    .3001811
-------------+----------------------------------------------------------------
identity_ur  |
     Discrim |   5.037059   1.566149     3.22   0.001     1.965141    8.108976
        Diff |
         =1  |   .2735187   .0585061     4.68   0.000     .1587621    .3882753
-------------+----------------------------------------------------------------
close_ur     |
     Discrim |   4.103538   .4486548     9.15   0.000     3.223526    4.983551
        Diff |
         =1  |   .6651114   .0417849    15.92   0.000     .5831526    .7470702
-------------+----------------------------------------------------------------
dumavote_ur  |
     Discrim |   5.145234   .6302222     8.16   0.000     3.909087    6.381381
        Diff |
         =1  |   .2868863   .0383177     7.49   0.000     .2117282    .3620445
-------------+----------------------------------------------------------------
ft_ur        |
     Discrim |   2.818716   .2006239    14.05   0.000     2.425203    3.212229
        Diff |
       >= 1  |  -1.293396   .0750936   -17.22   0.000    -1.440688   -1.146104
       >= 2  |  -1.041936   .0665441   -15.66   0.000    -1.172459   -.9114136
       >= 3  |  -.9006997   .0626165   -14.38   0.000    -1.023519   -.7778807
       >= 4  |  -.6586761   .0576296   -11.43   0.000    -.7717136   -.5456387
       >= 5  |  -.5589669   .0553383   -10.10   0.000      -.66751   -.4504238
       >= 6  |   .0221901   .0462665     0.48   0.632    -.0685591    .1129394
       >= 7  |   .2980857    .044284     6.73   0.000     .2112251    .3849463
       >= 8  |   .5427577    .045026    12.05   0.000     .4544417    .6310738
       >= 9  |   .7842531   .0476452    16.46   0.000     .6907995    .8777067
        =10  |   .9640859   .0516414    18.67   0.000     .8627939    1.065378
------------------------------------------------------------------------------

. predict thetaur, latent
(option ebmeans assumed)
(using 30 quadrature points)

. polychoric thetaur prefexp_ur prefimp_ur identity_ur close_ur, pw verbose

Variables :  thetaur prefexp_ur
Type :       polyserial
Rho        = .85087514
S.e.       = .0227232

Variables :  thetaur prefimp_ur
Type :       polyserial
Rho        = .96543743
S.e.       = .00572229

Variables :  thetaur identity_ur
Type :       polyserial
Rho        = .9571863
S.e.       = .00818907

Variables :  thetaur close_ur
Type :       polyserial
Rho        = .93968079
S.e.       = .0060679
no observations

Variables :  prefexp_ur prefimp_ur
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .
no observations

Variables :  prefexp_ur identity_ur
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefexp_ur close_ur
Type :       polychoric
Rho        = .82128419
S.e.       = .0364693
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .
no observations

Variables :  prefimp_ur identity_ur
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefimp_ur close_ur
Type :       polychoric
Rho        = .86801032
S.e.       = .02427947
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  identity_ur close_ur
Type :       polychoric
Rho        = .89545975
S.e.       = .02600967
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

. 
. drop thetaur

. svy: irt grm prefexp_ur identity_ur close_ur dumavote_ur ft_ur , intpoints(30) difficult intmethod(mcaghermite) 
(running irt on estimation sample)

Survey: Graded response model

Number of strata   =         1                  Number of obs     =      1,602
Number of PSUs     =     1,602                  Population size   =      1,602
                                                Design df         =      1,601

------------------------------------------------------------------------------
             |             Linearized
             |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
prefexp_ur   |
     Discrim |   9.012052   3.689919     2.44   0.015     1.774473    16.24963
        Diff |
         =1  |   .2212979    .050715     4.36   0.000      .121823    .3207728
-------------+----------------------------------------------------------------
identity_ur  |
     Discrim |   5.018138   1.573761     3.19   0.001      1.93129    8.104987
        Diff |
         =1  |   .2710838   .0585619     4.63   0.000     .1562178    .3859499
-------------+----------------------------------------------------------------
close_ur     |
     Discrim |   3.836915   .4098938     9.36   0.000      3.03293      4.6409
        Diff |
         =1  |   .6720474   .0427702    15.71   0.000     .5881559     .755939
-------------+----------------------------------------------------------------
dumavote_ur  |
     Discrim |   5.293128   .6861397     7.71   0.000     3.947302    6.638955
        Diff |
         =1  |   .2824526   .0386646     7.31   0.000     .2066141    .3582911
-------------+----------------------------------------------------------------
ft_ur        |
     Discrim |   2.861296   .2172378    13.17   0.000     2.435195    3.287396
        Diff |
       >= 1  |  -1.282334   .0751567   -17.06   0.000     -1.42975   -1.134918
       >= 2  |   -1.03284    .066561   -15.52   0.000    -1.163396   -.9022842
       >= 3  |  -.8929396   .0625835   -14.27   0.000    -1.015694   -.7701854
       >= 4  |  -.6540112   .0572308   -11.43   0.000    -.7662664    -.541756
       >= 5  |  -.5556347   .0550699   -10.09   0.000    -.6636514   -.4476181
       >= 6  |   .0188157   .0462471     0.41   0.684    -.0718955     .109527
       >= 7  |   .2937665   .0440645     6.67   0.000     .2073363    .3801966
       >= 8  |   .5397011   .0450414    11.98   0.000     .4513548    .6280474
       >= 9  |   .7819471   .0477029    16.39   0.000     .6883805    .8755138
        =10  |   .9610903   .0517881    18.56   0.000     .8595106     1.06267
------------------------------------------------------------------------------

. predict thetaur, latent
(option ebmeans assumed)
(using 30 quadrature points)

. polychoric thetaur prefexp_ur prefimp_ur identity_ur close_ur, pw verbose

Variables :  thetaur prefexp_ur
Type :       polyserial
Rho        = .96878786
S.e.       = .0055168

Variables :  thetaur prefimp_ur
Type :       polyserial
Rho        = .84572668
S.e.       = .01647051

Variables :  thetaur identity_ur
Type :       polyserial
Rho        = .95667926
S.e.       = .00811982

Variables :  thetaur close_ur
Type :       polyserial
Rho        = .92699347
S.e.       = .00647398
no observations

Variables :  prefexp_ur prefimp_ur
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .
no observations

Variables :  prefexp_ur identity_ur
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefexp_ur close_ur
Type :       polychoric
Rho        = .82128421
S.e.       = .0364693
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .
no observations

Variables :  prefimp_ur identity_ur
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefimp_ur close_ur
Type :       polychoric
Rho        = .86801036
S.e.       = .02427947
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  identity_ur close_ur
Type :       polychoric
Rho        = .89545971
S.e.       = .02600968
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

. 
. drop thetaur

. svy: irt grm prefexp_ur prefimp_ur close_ur dumavote_ur ft_ur , intpoints(30) difficult intmethod(mcaghermite) 
(running irt on estimation sample)

Survey: Graded response model

Number of strata   =         1                  Number of obs     =      1,602
Number of PSUs     =     1,602                  Population size   =      1,602
                                                Design df         =      1,601

------------------------------------------------------------------------------
             |             Linearized
             |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
prefexp_ur   |
     Discrim |   8.818828   3.302113     2.67   0.008      2.34191    15.29575
        Diff |
         =1  |   .2306934   .0504295     4.57   0.000     .1317787    .3296081
-------------+----------------------------------------------------------------
prefimp_ur   |
     Discrim |   5.915235   1.115684     5.30   0.000     3.726881    8.103589
        Diff |
         =1  |   .2137591   .0466661     4.58   0.000     .1222261     .305292
-------------+----------------------------------------------------------------
close_ur     |
     Discrim |    3.83467   .3898795     9.84   0.000     3.069942    4.599397
        Diff |
         =1  |   .6736465   .0422841    15.93   0.000     .5907084    .7565846
-------------+----------------------------------------------------------------
dumavote_ur  |
     Discrim |   5.643482   .6938877     8.13   0.000     4.282459    7.004506
        Diff |
         =1  |    .292155   .0381698     7.65   0.000     .2172869    .3670232
-------------+----------------------------------------------------------------
ft_ur        |
     Discrim |   2.927152   .2050888    14.27   0.000     2.524882    3.329423
        Diff |
       >= 1  |  -1.277317   .0751706   -16.99   0.000     -1.42476   -1.129874
       >= 2  |  -1.022906   .0667575   -15.32   0.000    -1.153848   -.8919651
       >= 3  |  -.8804343   .0628367   -14.01   0.000    -1.003685   -.7571834
       >= 4  |  -.6374456    .057538   -11.08   0.000    -.7503034   -.5245878
       >= 5  |  -.5378238   .0552528    -9.73   0.000    -.6461993   -.4294484
       >= 6  |   .0377007   .0460541     0.82   0.413     -.052632    .1280335
       >= 7  |   .3082886   .0434884     7.09   0.000     .2229885    .3935887
       >= 8  |   .5479284   .0440941    12.43   0.000     .4614401    .6344168
       >= 9  |   .7828755   .0464214    16.86   0.000     .6918224    .8739285
        =10  |   .9571898   .0501786    19.08   0.000     .8587671    1.055612
------------------------------------------------------------------------------

. predict thetaur, latent
(option ebmeans assumed)
(using 30 quadrature points)

. polychoric thetaur prefexp_ur prefimp_ur identity_ur close_ur, pw verbose

Variables :  thetaur prefexp_ur
Type :       polyserial
Rho        = .96649854
S.e.       = .00572879

Variables :  thetaur prefimp_ur
Type :       polyserial
Rho        = .96393963
S.e.       = .00557072

Variables :  thetaur identity_ur
Type :       polyserial
Rho        = .82897569
S.e.       = .02435992

Variables :  thetaur close_ur
Type :       polyserial
Rho        = .92514028
S.e.       = .00682251
no observations

Variables :  prefexp_ur prefimp_ur
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .
no observations

Variables :  prefexp_ur identity_ur
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefexp_ur close_ur
Type :       polychoric
Rho        = .82128419
S.e.       = .0364693
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .
no observations

Variables :  prefimp_ur identity_ur
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefimp_ur close_ur
Type :       polychoric
Rho        = .86801035
S.e.       = .02427947
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  identity_ur close_ur
Type :       polychoric
Rho        = .89545977
S.e.       = .02600967
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

. 
. /*
> drop thetaur
> svy: irt grm prefexp_ur prefimp_ur identity_ur dumavote_ur ft_ur , intpoints(60) difficult intmethod(mcaghermite) 
> predict thetaur, latent
> polychoric thetaur prefexp_ur prefimp_ur identity_ur close_ur, pw verbose
> */
. 
. *2: LDPR
. capture drop thetaLDPR

. svy: irt grm prefimp_LDPR identity_LDPR close_LDPR dumavote_LDPR ft_LDPR , intpoints(60) difficult intmethod(mcaghermite)
(running irt on estimation sample)

Survey: Graded response model

Number of strata   =         1                  Number of obs     =      1,602
Number of PSUs     =     1,602                  Population size   =      1,602
                                                Design df         =      1,601

------------------------------------------------------------------------------
             |             Linearized
             |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
prefimp_LDPR |
     Discrim |   8.600836   4.118746     2.09   0.037     .5221349    16.67954
        Diff |
         =1  |   1.492955   .0673254    22.18   0.000       1.3609    1.625011
-------------+----------------------------------------------------------------
identity_L~R |
     Discrim |   8.161649    4.76803     1.71   0.087    -1.190589    17.51389
        Diff |
         =1  |   1.545604   .0859847    17.98   0.000      1.37695    1.714259
-------------+----------------------------------------------------------------
close_LDPR   |
     Discrim |   7.810088   2.266983     3.45   0.001     3.363521    12.25666
        Diff |
         =1  |   1.719292   .0729851    23.56   0.000     1.576136    1.862449
-------------+----------------------------------------------------------------
dumavote_L~R |
     Discrim |    9.04325   3.997863     2.26   0.024     1.201655    16.88485
        Diff |
         =1  |   1.590685   .0662462    24.01   0.000     1.460747    1.720624
-------------+----------------------------------------------------------------
ft_LDPR      |
     Discrim |   2.039288   .2606671     7.82   0.000     1.528003    2.550572
        Diff |
       >= 1  |  -.8825119   .0774276   -11.40   0.000    -1.034382   -.7306418
       >= 2  |  -.4535531   .0613065    -7.40   0.000    -.5738025   -.3333038
       >= 3  |   -.186863   .0554771    -3.37   0.001    -.2956784   -.0780476
       >= 4  |   .0975036   .0534384     1.82   0.068    -.0073129    .2023202
       >= 5  |   .3079679   .0550768     5.59   0.000     .1999377    .4159982
       >= 6  |   1.317106   .0941768    13.99   0.000     1.132383    1.501829
       >= 7  |   1.616433   .1132221    14.28   0.000     1.394354    1.838512
       >= 8  |   1.890102   .1329028    14.22   0.000      1.62942    2.150784
       >= 9  |   2.107007   .1527037    13.80   0.000     1.807487    2.406527
        =10  |   2.281507   .1666095    13.69   0.000     1.954712    2.608303
------------------------------------------------------------------------------

. predict thetaLDPR, latent
(option ebmeans assumed)
(using 60 quadrature points)

. polychoric thetaLDPR prefexp_LDPR prefimp_LDPR identity_LDPR close_LDPR , pw verbose

Variables :  thetaLDPR prefexp_LDPR
Type :       polyserial
Rho        = .7753217
S.e.       = .04522391

Variables :  thetaLDPR prefimp_LDPR
Type :       polyserial
Rho        = .90820381
S.e.       = .02003859

Variables :  thetaLDPR identity_LDPR
Type :       polyserial
Rho        = .89384761
S.e.       = .02528233

Variables :  thetaLDPR close_LDPR
Type :       polyserial
Rho        = .82675707
S.e.       = .01567911
no observations

Variables :  prefexp_LDPR prefimp_LDPR
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .
no observations

Variables :  prefexp_LDPR identity_LDPR
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefexp_LDPR close_LDPR
Type :       polychoric
Rho        = .92833877
S.e.       = .04094697
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .
no observations

Variables :  prefimp_LDPR identity_LDPR
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefimp_LDPR close_LDPR
Type :       polychoric
Rho        = .95994632
S.e.       = .01833272
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  identity_LDPR close_LDPR
Type :       polychoric
Rho        = .95872359
S.e.       = .03009851
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

. 
. drop thetaLDPR

. svy: irt grm prefexp_LDPR identity_LDPR close_LDPR dumavote_LDPR ft_LDPR , intpoints(60) difficult intmethod(mcaghermite)
(running irt on estimation sample)

Survey: Graded response model

Number of strata   =         1                  Number of obs     =      1,602
Number of PSUs     =     1,602                  Population size   =      1,602
                                                Design df         =      1,601

------------------------------------------------------------------------------
             |             Linearized
             |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
prefexp_LDPR |
     Discrim |   8.461475    4.97629     1.70   0.089    -1.299253     18.2222
        Diff |
         =1  |   1.507117   .0848468    17.76   0.000     1.340695    1.673539
-------------+----------------------------------------------------------------
identity_L~R |
     Discrim |   9.779208    8.34825     1.17   0.242    -6.595441    26.15386
        Diff |
         =1  |   1.528724   .0786713    19.43   0.000     1.374414    1.683033
-------------+----------------------------------------------------------------
close_LDPR   |
     Discrim |   8.610825   2.990968     2.88   0.004       2.7442    14.47745
        Diff |
         =1  |    1.70779    .074004    23.08   0.000     1.562635    1.852945
-------------+----------------------------------------------------------------
dumavote_L~R |
     Discrim |   7.880348    2.77515     2.84   0.005     2.437039    13.32366
        Diff |
         =1  |   1.602368   .0697974    22.96   0.000     1.465464    1.739272
-------------+----------------------------------------------------------------
ft_LDPR      |
     Discrim |   2.219174   .2884099     7.69   0.000     1.653474    2.784875
        Diff |
       >= 1  |  -.8538518   .0734094   -11.63   0.000    -.9978404   -.7098632
       >= 2  |  -.4357024   .0588377    -7.41   0.000    -.5511094   -.3202955
       >= 3  |  -.1760471   .0538059    -3.27   0.001    -.2815846   -.0705096
       >= 4  |   .1006486   .0520372     1.93   0.053    -.0014196    .2027167
       >= 5  |   .3055249   .0533795     5.72   0.000     .2008239     .410226
       >= 6  |   1.282103   .0875204    14.65   0.000     1.110436    1.453769
       >= 7  |   1.567844   .1050946    14.92   0.000     1.361707    1.773982
       >= 8  |   1.830381   .1230874    14.87   0.000     1.588952    2.071811
       >= 9  |   2.038514   .1404718    14.51   0.000     1.762986    2.314042
        =10  |   2.204366    .153931    14.32   0.000     1.902439    2.506294
------------------------------------------------------------------------------

. predict thetaLDPR, latent ebmodes

. polychoric thetaLDPR prefexp_LDPR prefimp_LDPR identity_LDPR close_LDPR , pw verbose

Variables :  thetaLDPR prefexp_LDPR
Type :       polyserial
Rho        = .92936426
S.e.       = .02063593

Variables :  thetaLDPR prefimp_LDPR
Type :       polyserial
Rho        = .70287277
S.e.       = .06103474

Variables :  thetaLDPR identity_LDPR
Type :       polyserial
Rho        = .94251378
S.e.       = .0197086

Variables :  thetaLDPR close_LDPR
Type :       polyserial
Rho        = .89840912
S.e.       = .01325941
no observations

Variables :  prefexp_LDPR prefimp_LDPR
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .
no observations

Variables :  prefexp_LDPR identity_LDPR
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefexp_LDPR close_LDPR
Type :       polychoric
Rho        = .9283384
S.e.       = .04094726
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .
no observations

Variables :  prefimp_LDPR identity_LDPR
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefimp_LDPR close_LDPR
Type :       polychoric
Rho        = .959946
S.e.       = .01833292
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  identity_LDPR close_LDPR
Type :       polychoric
Rho        = .95872341
S.e.       = .03009869
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

. 
. drop thetaLDPR

. svy: irt grm prefexp_LDPR prefimp_LDPR close_LDPR dumavote_LDPR ft_LDPR , intpoints(60) difficult intmethod(mcaghermite)
(running irt on estimation sample)

Survey: Graded response model

Number of strata   =         1                  Number of obs     =      1,602
Number of PSUs     =     1,602                  Population size   =      1,602
                                                Design df         =      1,601

------------------------------------------------------------------------------
             |             Linearized
             |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
prefexp_LDPR |
     Discrim |   7.143104   3.728113     1.92   0.056    -.1693921     14.4556
        Diff |
         =1  |   1.511919   .0828152    18.26   0.000     1.349482    1.674357
-------------+----------------------------------------------------------------
prefimp_LDPR |
     Discrim |   7.827884   3.468531     2.26   0.024     1.024546    14.63122
        Diff |
         =1  |   1.486407   .0792021    18.77   0.000     1.331056    1.641757
-------------+----------------------------------------------------------------
close_LDPR   |
     Discrim |   6.815007   1.746762     3.90   0.000     3.388827    10.24119
        Diff |
         =1  |   1.719908   .0807491    21.30   0.000     1.561523    1.878293
-------------+----------------------------------------------------------------
dumavote_L~R |
     Discrim |   21.68922   57.97619     0.37   0.708      -92.028    135.4064
        Diff |
         =1  |   1.551032   .1336967    11.60   0.000     1.288793    1.813271
-------------+----------------------------------------------------------------
ft_LDPR      |
     Discrim |   2.017411   .2509802     8.04   0.000     1.525127    2.509695
        Diff |
       >= 1  |   -.889254   .0793395   -11.21   0.000    -1.044874   -.7336338
       >= 2  |  -.4588815   .0639037    -7.18   0.000    -.5842253   -.3335378
       >= 3  |  -.1914397   .0586315    -3.27   0.001    -.3064423    -.076437
       >= 4  |   .0940822   .0569405     1.65   0.099    -.0176035    .2057679
       >= 5  |   .3057766   .0586317     5.22   0.000     .1907737    .4207795
       >= 6  |   1.319885   .0956215    13.80   0.000     1.132328    1.507441
       >= 7  |   1.620538   .1139861    14.22   0.000      1.39696    1.844116
       >= 8  |   1.894326   .1322829    14.32   0.000      1.63486    2.153792
       >= 9  |   2.110623   .1517336    13.91   0.000     1.813005     2.40824
        =10  |   2.284548   .1687006    13.54   0.000      1.95365    2.615445
------------------------------------------------------------------------------

. predict thetaLDPR, latent
(option ebmeans assumed)
(using 60 quadrature points)

. polychoric thetaLDPR prefexp_LDPR prefimp_LDPR identity_LDPR close_LDPR , pw verbose

Variables :  thetaLDPR prefexp_LDPR
Type :       polyserial
Rho        = .88706812
S.e.       = .02932477

Variables :  thetaLDPR prefimp_LDPR
Type :       polyserial
Rho        = .89865992
S.e.       = .02336894

Variables :  thetaLDPR identity_LDPR
Type :       polyserial
Rho        = .70783562
S.e.       = .05361637

Variables :  thetaLDPR close_LDPR
Type :       polyserial
Rho        = .81062994
S.e.       = .017258
no observations

Variables :  prefexp_LDPR prefimp_LDPR
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .
no observations

Variables :  prefexp_LDPR identity_LDPR
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefexp_LDPR close_LDPR
Type :       polychoric
Rho        = .92833877
S.e.       = .04094697
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .
no observations

Variables :  prefimp_LDPR identity_LDPR
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  prefimp_LDPR close_LDPR
Type :       polychoric
Rho        = .95994612
S.e.       = .01833282
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  identity_LDPR close_LDPR
Type :       polychoric
Rho        = .95872341
S.e.       = .03009869
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

. 
. /*
> drop thetaLDPR
> svy: irt grm prefexp_LDPR prefimp_LDPR identity_LDPR dumavote_LDPR ft_LDPR , intpoints(60) difficult intmethod(mcaghermite)
> predict thetaLDPR, latent
> polychoric thetaLDPR prefexp_LDPR prefimp_LDPR identity_LDPR close_LDPR , pw verbose
> */
. 
. *3:COMM
. capture drop thetacomm

. irt grm identity_comm close_comm dumavote_comm ft_comm , intpoints(60) difficult intmethod(mcaghermite)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3552.7566  
Iteration 1:   log likelihood = -3552.7566  (backed up)

Fitting full model:

Iteration 0:   log likelihood = -3404.5577  
Iteration 1:   log likelihood = -3157.8104  
Iteration 2:   log likelihood = -3119.1902  
Iteration 3:   log likelihood = -3112.8359  
Iteration 4:   log likelihood = -3110.8311  
Iteration 5:   log likelihood = -3110.5877  
Iteration 6:   log likelihood = -3110.5519  
Iteration 7:   log likelihood = -3110.5475  
Iteration 8:   log likelihood = -3110.5472  

Graded response model                           Number of obs     =      1,602
Log likelihood = -3110.5472
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
identity_c~m |
     Discrim |    7.47619   3.259474     2.29   0.022     1.087738    13.86464
        Diff |
         =1  |   1.527443   .1012567    15.08   0.000     1.328983    1.725902
-------------+----------------------------------------------------------------
close_comm   |
     Discrim |   4.937451   .8551604     5.77   0.000     3.261367    6.613534
        Diff |
         =1  |   1.624739   .0884098    18.38   0.000     1.451459    1.798019
-------------+----------------------------------------------------------------
dumavote_c~m |
     Discrim |   11.07322   10.32613     1.07   0.284    -9.165611    31.31206
        Diff |
         =1  |   1.453015   .0498156    29.17   0.000     1.355378    1.550652
-------------+----------------------------------------------------------------
ft_comm      |
     Discrim |   2.580226   .2575918    10.02   0.000     2.075355    3.085096
        Diff |
       >= 1  |  -.9793648   .0579217   -16.91   0.000    -1.092889   -.8658403
       >= 2  |   -.614383   .0496546   -12.37   0.000    -.7117042   -.5170618
       >= 3  |  -.3740227   .0462827    -8.08   0.000    -.4647351   -.2833104
       >= 4  |   -.106076   .0448259    -2.37   0.018    -.1939331   -.0182188
       >= 5  |   .1297715   .0458269     2.83   0.005     .0399525    .2195905
       >= 6  |   1.017729   .0672981    15.12   0.000     .8858274    1.149631
       >= 7  |   1.290691   .0779323    16.56   0.000     1.137947    1.443436
       >= 8  |   1.494513   .0875379    17.07   0.000     1.322941    1.666084
       >= 9  |   1.727316   .0996558    17.33   0.000     1.531995    1.922638
        =10  |    1.84461   .1062454    17.36   0.000     1.636373    2.052847
------------------------------------------------------------------------------

. predict thetacomm, latent ebmodes

. polychoric thetacomm pref_comm identity_comm close_comm dumavote_comm ft_comm , pw verbose 

Variables :  thetacomm pref_comm
Type :       polyserial
Rho        = .88163693
S.e.       = .0155383

Variables :  thetacomm identity_comm
Type :       polyserial
Rho        = .93970987
S.e.       = .01751517

Variables :  thetacomm close_comm
Type :       polyserial
Rho        = .90815339
S.e.       = .01012415

Variables :  thetacomm dumavote_comm
Type :       polyserial
Rho        = .96627769
S.e.       = .00597819

Variables :  thetacomm ft_comm
Type :       Pearson
Rho        = .95804359
S.e.       = .0081626
no observations

Variables :  pref_comm identity_comm
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  pref_comm close_comm
Type :       polychoric
Rho        = .94455456
S.e.       = .01586883
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  pref_comm dumavote_comm
Type :       polychoric
Rho        = .97501904
S.e.       = .00858681
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  pref_comm ft_comm
Type :       polyserial
Rho        = .79805026
S.e.       = .03113275

Variables :  identity_comm close_comm
Type :       polychoric
Rho        = .91005191
S.e.       = .04013188
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  identity_comm dumavote_comm
Type :       polychoric
Rho        = .95947545
S.e.       = .02141648
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  identity_comm ft_comm
Type :       polyserial
Rho        = .81934814
S.e.       = .03779777

Variables :  close_comm dumavote_comm
Type :       polychoric
Rho        = .94188488
S.e.       = .01417307
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  close_comm ft_comm
Type :       polyserial
Rho        = .74091472
S.e.       = .03052289

Variables :  dumavote_comm ft_comm
Type :       polyserial
Rho        = .77088813
S.e.       = .02799207

. 
. drop thetacomm

. irt grm pref_comm close_comm dumavote_comm ft_comm , intpoints(60) difficult intmethod(mcaghermite)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3787.2209  
Iteration 1:   log likelihood = -3787.2209  

Fitting full model:

Iteration 0:   log likelihood = -3563.3829  
Iteration 1:   log likelihood = -3240.0711  
Iteration 2:   log likelihood = -3179.6473  
Iteration 3:   log likelihood = -3171.5302  
Iteration 4:   log likelihood = -3168.6038  
Iteration 5:   log likelihood = -3168.5767  
Iteration 6:   log likelihood = -3168.5767  

Graded response model                           Number of obs     =      1,602
Log likelihood = -3168.5767
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
pref_comm    |
     Discrim |   10.32466     2.8189     3.66   0.000     4.799722    15.84961
        Diff |
         =1  |   1.370856   .0609511    22.49   0.000     1.251394    1.490318
-------------+----------------------------------------------------------------
close_comm   |
     Discrim |   5.269977   .7335027     7.18   0.000     3.832339    6.707616
        Diff |
         =1  |   1.600335   .0655834    24.40   0.000     1.471794    1.728876
-------------+----------------------------------------------------------------
dumavote_c~m |
     Discrim |   9.435731   2.302485     4.10   0.000     4.922944    13.94852
        Diff |
         =1  |   1.442744   .0554538    26.02   0.000     1.334057    1.551432
-------------+----------------------------------------------------------------
ft_comm      |
     Discrim |   2.637259   .2221238    11.87   0.000     2.201905    3.072614
        Diff |
       >= 1  |  -.9724663   .0563079   -17.27   0.000    -1.082828   -.8621049
       >= 2  |  -.6098193   .0490102   -12.44   0.000    -.7058775    -.513761
       >= 3  |   -.370959   .0456946    -8.12   0.000    -.4605187   -.2813992
       >= 4  |  -.1046415   .0434931    -2.41   0.016    -.1898864   -.0193966
       >= 5  |    .129977    .043038     3.02   0.003     .0456241    .2143299
       >= 6  |   1.011581   .0556742    18.17   0.000     .9024613      1.1207
       >= 7  |   1.281742   .0644137    19.90   0.000     1.155493     1.40799
       >= 8  |     1.4837   .0722974    20.52   0.000     1.341999      1.6254
       >= 9  |    1.71417   .0826628    20.74   0.000     1.552154    1.876186
        =10  |   1.830366   .0885141    20.68   0.000     1.656881     2.00385
------------------------------------------------------------------------------

. predict thetacomm, latent ebmodes

. polychoric thetacomm pref_comm identity_comm close_comm dumavote_comm ft_comm , pw verbose 

Variables :  thetacomm pref_comm
Type :       polyserial
Rho        = .96051299
S.e.       = .008671

Variables :  thetacomm identity_comm
Type :       polyserial
Rho        = .80270676
S.e.       = .04865026

Variables :  thetacomm close_comm
Type :       polyserial
Rho        = .91079827
S.e.       = .00968648

Variables :  thetacomm dumavote_comm
Type :       polyserial
Rho        = .95698753
S.e.       = .00699398

Variables :  thetacomm ft_comm
Type :       Pearson
Rho        = .95586764
S.e.       = .00836694
no observations

Variables :  pref_comm identity_comm
Type :       polychoric
Rho        = .
S.e.       = .
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  pref_comm close_comm
Type :       polychoric
Rho        = .94455458
S.e.       = .0158688
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  pref_comm dumavote_comm
Type :       polychoric
Rho        = .97501904
S.e.       = .00858681
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  pref_comm ft_comm
Type :       polyserial
Rho        = .79805026
S.e.       = .03113275

Variables :  identity_comm close_comm
Type :       polychoric
Rho        = .91005196
S.e.       = .04013186
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  identity_comm dumavote_comm
Type :       polychoric
Rho        = .95947547
S.e.       = .02141647
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  identity_comm ft_comm
Type :       polyserial
Rho        = .81934814
S.e.       = .03779777

Variables :  close_comm dumavote_comm
Type :       polychoric
Rho        = .94188488
S.e.       = .01417307
Goodness of fit tests:
Pearson G2 = ., Prob( >chi2(.)) = .
LR X2      = ., Prob( >chi2(.)) = .

Variables :  close_comm ft_comm
Type :       polyserial
Rho        = .74091472
S.e.       = .03052289

Variables :  dumavote_comm ft_comm
Type :       polyserial
Rho        = .77088813
S.e.       = .02799207

. 
. /*
> capture drop thetacomm
> irt grm pref_comm identity_comm dumavote_comm ft_comm , intpoints(60) difficult intmethod(mcaghermite)
> predict thetacomm, latent ebmodes
> polychoric thetacomm pref_comm identity_comm close_comm dumavote_comm ft_comm , pw verbose 
> */
. 
. log close
      name:  <unnamed>
       log:  C:\Users\Andy baker\OneDrive - University of Colorado at Boulder Office 365\My Documents\Research\Paper PID Experiment\Russia\JOP IRT Russia Res
> ults.log
  log type:  text
 closed on:   6 Mar 2018, 21:11:50
-------------------------------------------------------------------------------------------------------------------------------------------------------------
